The seven-minute video explains in simple terms how important GPS has become to everyday life — for aircraft and ship navigation, global financial transactions, precision agriculture, weather forecasting, disaster relief, and, of course, smartphones.

The recent testing of GPS III space vehicle 1 (SV 1) bus — the portion of the space vehicle that carries mission payloads and hosts them in orbit — assured that all bus subsystems are functioning normally and ready for final integration with the satellite’s navigation payload. Systems tested included: guidance, navigation and control; command and data handling; on-board computer and flight software; environmental controls; and electrical power regulation. The SV 1 satellite’s network communication equipment subsystem that interfaces with the ground control segment and distributes data throughout the space vehicle also passed all tests as expected.

This milestone follows February’s successful initial power-on of SV 1, which demonstrated the electrical-mechanical integration, validated the satellite’s interfaces, and led the way for functional and hardware-software integration testing.

“The successful completion of the SV 1 bus functional check out validates that the spacecraft is now ready to begin the next sequence of payload integration and environmental testing, prior to delivery,” explained Keoki Jackson, vice president of Lockheed Martin’s Navigation Systems mission area.

GPS III SV 1′s navigation payload, which is being produced by ITT Exelis, will be delivered to Lockheed Martin’s GPS Processing Facility (GPF) near Denver later in 2013. The hosted nuclear detection system payload has already been delivered and mechanically integrated. The satellite remains on schedule for flight-ready delivery to the U.S. Air Force in 2014.

GPS III is a critically important program for the Air Force, affordably replacing aging GPS satellites in orbit, while improving capability to meet the evolving demands of military, commercial and civilian users. GPS III satellites will deliver three times better accuracy and — to outpace growing global threats that could disrupt GPS service — up to eight times improved anti-jamming signal power for additional resiliency. The GPS III will also include enhancements adding to the spacecraft’s design life and a new civil signal designed to be interoperable with international global navigation satellite systems.

The U.S. Air Force has produced a video about the GPS satellite modernization program:

Lockheed Martin is under contract for production of the first four GPS III satellites (SV 1-4), and has received advanced procurement funding for long-lead components for the fifth, sixth, seventh and eighth satellites (SV 5-8).

The GPS III team is led by the Global Positioning Systems Directorate at the U.S. Air Force Space and Missile Systems Center. Lockheed Martin is the GPS III prime contractor with teammates ITT Exelis, General Dynamics, Infinity Systems Engineering, Honeywell, ATK and other subcontractors. Air Force Space Command’s 2nd Space Operations Squadron (2SOPS), based at Schriever Air Force Base, Colo., manages and operates the GPS constellation for both civil and military users.

Headquartered in Bethesda, Maryland, Lockheed Martin is a global security and aerospace company that employs about 118,000 people worldwide and is principally engaged in the research, design, development, manufacture, integration, and sustainment of advanced technology systems, products, and services. The corporation’s net sales for 2012 were $47.2 billion.

This course will help attendees develop proficiency with advanced receiver processing of current, modernized, and new signals from GPS, GLONASS, Galileo, BeiDou, and QZSS. It teaches systems engineering skills, along with techniques for receiver processing and for assessing processing performance. Review problems, worked in class, help students understand and apply the key concepts.

Those who attend will become proficient with the essential aspects of using GPS and GNSS signals.

Course days:
Friday, Saturday, September 13-14
Monday, Tuesday, September 16-17

Instructor: Dr. John Betz, MITRE

For more information, visit the Navtech website.

According to information shared by the Russian Legal Information Agency, the Investigative Committee’s department for the Moscow Region has launched a preliminary probe into the case.

Construction of the GLONASS satellite navigation system control and support center began in June 2010 on the site used by TsNIImash, the head research company of Russia’s federal space agency. The center was supposed to hold equipment for collecting and processing the data supplied by the GLONASS global network.

The construction was financed by a federal program, with 1.050 billion ($33.22 million) allocated for the project. By the end of 2010, it came to light that construction costs had been overstated, Izvestia reports. An expert appraisal revealed that the contractor had rigged the costs. The government did not allocate additional funds, so construction was suspended in December 2011 when the Federal GLONASS Program for 2002-2011 ended. The construction of the building has never been completed.

In November 2012, the general designer of GLONASS, Yuri Urlichich, was dismissed from his post as a result of the scandal.

IRNSS, an independent navigation satellite system being developed by India, will have a constellation of seven satellites that enables its users to determine their location and time accurately. These satellites will be positioned in geostationary and inclined geosynchronous orbits 36,000 kilometers above the Earth’s surface. IRNSS coverage will extend over India and the southeast Asia region. The satellites are equipped with high-precision atomic clocks and continuously transmit navigation signals to users.

As the focal point of many critical operations of IRNSS, the ISRO Navigation Centre (INC) is responsible for providing the time reference, generation of navigation messages, and monitoring and control of ground facilities including ranging stations of IRNSS. It hosts several key technical facilities for supporting various navigation functions.

Key to the navigation support is the time reference to which all ground systems and the satellite clocks are synchronized. This time reference is generated by the high-precision timing facility located at INC. This timing facility is equipped with high-stability, high-precision atomic clocks to provide stable and continuous time reference to the navigation system.

IRNSS will have a network of 21 ranging stations geographically distributed primarily across India. They provide data for the orbit determination of IRNSS satellites and monitoring of the navigation signals. The data from the ranging/monitoring stations is sent to the data processing facility at INC where it is processed to generate the navigation messages. The navigation messages are then transmitted from INC to IRNSS satellites through the spacecraft control facility at Hassan/Bhopal. The data processing and storage facilities at INC enable swift processing of data and support its systematic storage.

INC is connected to the ranging stations and to the satellite control facilities through two highly reliable dedicated communication networks consisting of satellite and terrestrial links. The hub for the satellite communication links is hosted at INC.

The INC was inaugurated by V. Narayanasamy, minister of state in the Indian prime minister’s office. Speaking on the occasion, Narayanasamy said he appreciated the commitment and dedication of Indian space scientists in realizing the objectives of the country’s space programme. The minister also gave away various awards instituted by Astronautical Society of India (ASI) and ISRO.

INNOVATION INSIGHTS with Richard Langley

IT’S A HOSTILE (ELECTRONIC) WORLD OUT THERE, PEOPLE. Our wired and radio-based communication systems are constantly under attack from evil doers. We are all familiar with computer viruses and worms hiding in malicious software or malware distributed over the Internet or by infected USB flash drives. Trojan horses are particularly insidious. These are programs concealing harmful code that can lead to many undesirable effects such as deleting a user’s files or installing additional harmful software. Such programs pass themselves off as benign, just like the “gift” the Greeks delivered to the Trojans as reported in Virgil’s Aeneid. This was a very early example of spoofing. Spoofing of Internet Protocol (IP) datagrams is particularly prevalent. They contain forged source IP addresses with the purpose of concealing the identity of the sender or impersonating another computing system.

To spoof someone or something is to deceive or hoax, passing off a deliberately fabricated falsehood made to masquerade as truth. The word “spoof” was introduced by the English stage comedian Arthur Roberts in the late 19th century. He invented a game of that name, which involved trickery and nonsense. Now, the most common use of the word is as a synonym for parody or satirize — rather benign actions. But it is the malicious use of spoofing that concerns users of electronic communications.

And it is not just wired communications that are susceptible to spoofing. Communications and other services using radio waves are, in principle, also spoofable. One of the first uses of radio-signal spoofing was in World War I when British naval shore stations sent transmissions using German ship call signs. In World War II, spoofing became an established military tactic and was extended to radar and navigation signals. For example, German bomber aircraft navigated using radio signals transmitted from ground stations in occupied Europe, which the British spoofed by transmitting similar signals on the same frequencies. They coined the term “meaconing” for the interception and rebroadcast of navigation signals (meacon = m(islead)+(b)eacon).

Fast forward to today. GPS and other GNSS are also susceptible to meaconing. From the outset, the GPS P code, intended for use by military and other so-called authorized users, was designed to be encrypted to prevent straightforward spoofing. The anti-spoofing is implemented using a secret “W” encryption code, resulting in the P(Y) code. The C/A code and the newer L2C and L5 codes do not have such protection; nor, for the most part, do the civil codes of other GNSS. But, it turns out, even the P(Y) code is not fully protected from sophisticated meaconing attacks.

So, is there anything that military or civil GNSS users can do, then, to guard against their receivers being spoofed by sophisticated false signals? In this month’s column, we take a look at a novel, yet relatively easily implemented technique that enables users to detect and sequester spoofed signals. It just might help make it a safer world for GNSS positioning, navigation, and timing.

“Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. To contact him, see the “Contributing Editors” section on page 4.

The radionavigation community has known about the dangers of GNSS spoofing for a long time, as highlighted in the 2001 Volpe Report (see Further Reading). Traditional receiver autonomous integrity monitoring (RAIM) had been considered a good spoofing defense. It assumes a dumb spoofer whose false signal produces a random pseudorange and large navigation solution residuals. The large errors are easy to detect, and given enough authentic signals, the spoofed signal(s) can be identified and ignored.

That spoofing model became obsolete at The Institute of Navigation’s GNSS 2008 meeting. Dr. Todd Humphreys introduced a new receiver/spoofer that could simultaneously spoof all signals in a self-consistent way undetectable to standard RAIM techniques. Furthermore, it could use its GNSS reception capabilities and its known geometry relative to the victim to overlay the false signals initially on top of the true ones. Slowly it could capture the receiver tracking loops by raising the spoofer power to be slightly larger than that of the true signals, and then it could drag the victim receiver off to false, but believable, estimates of its position, time, or both.

Two of the authors of this article contributed to Humphreys’ initial developments. There was no intention to help bad actors deceive GNSS user equipment (UE). Rather, our goal was to field a formidable “Red Team” as part of a “Red Team/Blue Team” (foe/friend) strategy for developing advanced “Blue Team” spoofing defenses.

This seemed like a fun academic game until mid-December 2011, when news broke that the Iranians had captured a highly classified Central Intelligence Agency drone, a stealth Lockheed Martin RQ-170 Sentinel, purportedly by spoofing its GPS equipment. Given our work in spoofing and detection, this event caused quite a stir in our Cornell University research group, in Humphreys’ University of Texas at Austin group, and in other places. The editor of this column even got involved in our extensive e-mail correspondence. Two key questions were: Wouldn’t a classified spy drone be equipped with a Selective Availability Anti-Spoofing Module (SAASM) receiver and, therefore, not be spoofable? Isn’t it difficult to knit together a whole sequence of false GPS position fixes that will guide a drone to land in a wrong location? These issues, when coupled with apparent inconsistencies in the Iranians’ story and visible damage to the drone, led us to discount the spoofing claim.

Developing a New Spoofing Defense

My views about the Iranian claims changed abruptly in mid-April 2012. Todd Humphreys phoned me about an upcoming test of GPS jammers, slated for June 2012 at White Sands Missile Range (WSMR), New Mexico. The Department of Homeland Security (DHS) had already spent months arranging these tests, but Todd revealed something new in that call: He had convinced the DHS to include a spoofing test that would use his latest “Red Team” device. The goal would be to induce a small GPS-guided unmanned aerial vehicle (UAV), in this case a helicopter, to land when it was trying to hover. “Wow”, I thought. “This will be a mini-replication of what the Iranians claimed to have done to our spy drone, and I’m sure that Todd will pull it off. I want to be there and see it.” Cornell already had plans to attend to test jammer tracking and geolocation, but we would have to come a day early to see the spoofing “fun” — if we could get permission from U.S. Air Force 746th Test Squadron personnel at White Sands.

The implications of the UAV test bounced around in my head that evening and the next morning on my seven-mile bike commute to work. During that ride, I thought of a scenario in which the Iranians might have mounted a meaconing attack against a SAASM-equipped drone. That is, they might possibly have received and re-broadcast the wide-band P(Y) code in a clever way that could have nudged the drone off course and into a relatively soft landing on Iranian territory.

In almost the next moment, I conceived a defense against such an attack. It involves small antenna motions at a high frequency, the measurement of corresponding carrier-phase oscillations, and the evaluation of whether the motions and phase oscillations are more consistent with spoofed signals or true signals. This approach would yield a good defense for civilian and military receivers against both spoofing and meaconing attacks. The remainder of this article describes this defense and our efforts to develop and test it.

It is one thing to conceive an idea, maybe a good idea. It is quite another thing to bring it to fruition. This idea seemed good enough and important enough to “birth” the conception. The needed follow-up efforts included two parts, one theoretical and the other experimental.

The theoretical work involved the development of signal models, hypothesis tests, analyses, and software. It culminated in analysis and truth-model simulation results, which showed that the system could be very practical, using only centimeters of motion and a fraction of a second of data to reliably differentiate between spoofing attacks and normal GNSS operation.

Theories and analyses can contain fundamental errors, or overlooked real-world effects can swamp the main theoretical effect. Therefore, an experimental prototype was quickly conceived, developed, and tested. It consisted of a very simple antenna-motion system, an RF data-recording device, and after-the-fact signal processing. The signal processing used Matlab to perform the spoofing detection calculations after using a C-language software radio to perform standard GPS acquisition and tracking.

Tests of the non-spoofed case could be conducted anywhere outdoors. Our initial tests occurred on a Cornell rooftop in Ithaca, New York. Tests of the spoofed case are harder. One cannot transmit live spoofing signals except with special permission at special times and in special places, for example, at WSMR in the upcoming June tests. Fortunately, the important geometric properties of spoofed signals can be simulated by using GPS signal reception at an outdoor antenna and re-radiation in an anechoic chamber from a single antenna. Such a system was made available to us by the NASA facility at Wallops Island, Virginia, and our simulated spoofed-case testing occurred in late April of last year. All of our data were processed before mid-May, and they provided experimental confirmation of our system’s efficacy. The final results were available exactly three busy weeks after the initial conception.

Although we were convinced about our new system, we felt that the wider GNSS community would like to see successful tests against live-signal attacks by a real spoofer. Therefore, we wanted very much to bring our system to WSMR for the June 2012 spoofing attack on the drone. We could set up our system near the drone so that it would be subject to the same malicious signals, but without the need to mount our clumsy prototype on a compact UAV helicopter. We were concerned, however, about the possibility of revealing our technology before we had been able to apply for patent protection. After some hesitation and discussions with our licensing and technology experts, we decided to bring our system to the WSMR test, but with a physical cover to keep it secret. The cover consisted of a large cardboard box, large enough to accommodate the needed antenna motions. The WSMR data were successfully collected using this method. Post-processing of the data demonstrated very reliable differentiation between spoofed and non-spoofed cases under live-signal conditions, as will be described in subsequent sections of this article.

System Architecture and Prototype

The components and geometry of one possible version of this system are shown in FIGURE 1. The figure shows three of the GNSS satellites whose signals would be tracked in the non-spoofed case: satellites j-1, j, and j+1. It also shows the potential location of a spoofer that could send false versions of the signals from these same satellites. The spoofer has a single transmission antenna. Satellites j-1, j, and j+1 are visible to the receiver antenna, but the spoofer could “hijack” the receiver’s tracking loops for these signals so that only the false spoofed versions of these signals would be tracked by the receiver.

Figure 1. Spoofing detection antenna articulation system geometry relative to base mount, GNSS satellites, and potential spoofer.

The receiver antenna mount enables its phase center to be moved with respect to the mounting base. In Figure 1, this motion system is depicted as an open kinematic chain consisting of three links with ball joints. This is just one example of how a system can be configured to allow antenna motion. Spoofing detection can work well with just one translational degree of freedom, such as a piston-like up-and-down motion that could be provided by a solenoid operating along the z_a articulation axis. It would be wise to cover the motion system with an optically opaque radome, if possible, to prevent a spoofer from defeating this system by sensing the high-frequency antenna motions and spoofing their effects on carrier phase.

Suppose that the antenna articulation time history in its local body-fixed (x_a, y_a, z_a) coordinate system is b_a(t). Then the received carrier phases are sensitive to the projections of this motion onto the line-of-sight (LOS) directions of the received signals. These projections are along  , , and  in the non-spoofed case, with   being the known unit direction vector from the jth GNSS satellite to the nominal antenna location. In the spoofed case, the projections are all along , regardless of which signal is being spoofed, with  being the unknown unit direction vector from the spoofer to the victim antenna. Thus, there will be differences between the carrier-phase responses of the different satellites in the non-spoofed case, but these differences will vanish in the spoofed case. This distinction lies at the heart of the new spoofing detection method. Given that a good GNSS receiver can easily distinguish quarter-cycle carrier-phase variations, it is expected that this system will be able to detect spoofing using antenna motions as small as 4.8 centimeters, that is, a quarter wavelength of the GPS L1 signal.

The UE receiver and spoofing detection block in Figure 1 consists of a standard GNSS receiver, a means of inputting the antenna motion sensor data, and additional signal processing downstream of the standard GNSS receiver operations. The latter algorithms use as inputs the beat carrier-phase measurements from a standard phase-locked loop (PLL).

It may be necessary to articulate the antenna at a frequency nearly equal to the bandwidth of the PLL (say, at 1 Hz or higher). In this case, special post-processing calculations might be required to reconstruct the high-frequency phase variations accurately before they can be used to detect spoofing. The needed post-processing uses the in-phase and quadrature accumulations of a phase discriminator to reconstruct the noisy phase differences between the true signal and the PLL numerically controlled oscillator (NCO) signal. These differences are added to the NCO phases to yield the full high-bandwidth variations.

We implemented the first prototype of this system with one-dimensional antenna motion by mounting its patch antenna on a cantilevered beam. It is shown in FIGURE 2. Motion is initiated by pulling on the string shown in the upper left-hand part of the figure. Release of the string gives rise to decaying sinusoidal oscillations that have a frequency of about 2 Hz.

Figure 2. Antenna articulation system for first prototype spoofing detector tests: a cantilevered beam that allows single-degree-of-freedom antenna phase-center vibration along a horizontal axis.

The remainder of the prototype system consisted of a commercial-off-the-shelf RF data recording device, off-line software receiver code, and off-line spoofing detection software. The prototype system lacked an antenna motion sensor. We compensated for this omission by implementing additional signal-processing calculations. They included off-line parameter identification of the decaying sinusoidal motions coupled with estimation of the oscillations’ initial amplitude and phase for any given detection.

This spoofing detection system is not the first to propose the use of antenna motion to uncover spoofing, and it is related to techniques that rely on multiple antennas. The present system makes three new contributions to the art of spoofing detection: First, it clearly explains why the measured carrier phases from a rapidly oscillating antenna provide a good means to detect spoofing. Second, it develops a precise spoofing detection hypothesis test for a moving-antenna system. Third, it demonstrates successful spoofing detection against live-signal attacks by a “Humphreys-class” spoofer.

Signal Model Theory and Verification

The spoofing detection test relies on mathematical models of the response of beat carrier phase to antenna motion. Reasonable models for the non-spoofed and spoofed cases are, respectively:

  (1a)

(1b)

where  is the received (negative) beat carrier phase of the authentic or spoofed satellite-j signal at the kth sample time  . The three-by-three direction cosines matrix A is the transformation from the reference system, in which the direction vectors   and  are defined, to the local body-axis system, in which the antenna motion b_a(t) is defined. λ is the nominal carrier wavelength. The terms involving the unknown polynomial coefficients , , and  model other low-frequency effects on carrier phase, including satellite motion, UE motion if its antenna articulation system is mounted on a vehicle, and receiver clock drift. The term  is the receiver phase noise. It is assumed to be a zero-mean, Gaussian, white-noise process whose variance depends on the receiver carrier-to-noise-density ratio and the sample/accumulation frequency.

If the motion of the antenna is one-dimensional, then b_a(t) takes the form , with  being the articulation direction in body-axis coordinates and r_a(t) being a known scalar antenna deflection amplitude time history. If one defines the articulation direction in reference coordinates as  , then the carrier-phase models in Equations (1a) and (1b) become

   (2a)

  (2b)

There is one important feature of these models for purposes of spoofing detection. In the non-spoofed case, the term that models the effects of antenna motion varies between GPS satellites because the  direction vector varies with j. The spoofed case lacks variation between the satellites because the one spoofer direction  replaces  for all of the spoofed satellites. This becomes clear when one compares the first terms on the right-hand sides of Eqsuations (1a) and (1b) for the 3-D motion case and on the right-hand sides of Equations (2a) and (2b) for the 1-D case.

The carrier-phase time histories in FIGURES 3 and 4 illustrate this principle. These data were collected at WSMR using the prototype antenna motion system of Figure 2. The carrier-phase time histories have been detrended by estimating the , , and coefficients in Equations (2a) and (2b) and subtracting off their effects prior to plotting. In Figure 3, all eight satellite signals exhibit similar decaying sinusoid time histories, but with differing amplitudes and some of them with sign changes. This is exactly what is predicted by the 1-D non-spoofed model in Equation (2a). All seven spoofed signals in Figure 4, however, exhibit identical decaying sinusoidal oscillations because the  term in Equation (2b) is the same for all of them.

Figure 3. Detrended carrier-phase data from multiple satellites for a typical non-spoofed case using a 1-D antenna articulation system.

Figure 4. Multiple satellites’ detrended carrier-phase data for a typical spoofed case using a 1-D antenna articulation system.

As an aside, an interesting feature of Figure 3 is its evidence of the workings of the prototype system. The ramping phases of all the signals from t = 0.4 seconds to t = 1.4 seconds correspond to the initial pull on the string shown in Figure 2, and the steady portion from t = 1.4 seconds to t = 2.25 seconds represents a period when the string was held fixed prior to release.

Spoofing Detection Hypothesis Test

A hypothesis test can precisely answer the question of which model best fits the observed data: Does carrier-phase sameness describe the data, as in Figure 4? Then the receiver is being spoofed. Alternatively, is carrier-phase differentness more reasonable, as per Figure 3? Then the signals are trustworthy.

A hypothesis test can be developed for any batch of carrier-phase data that spans a sufficiently rich antenna motion profile b_a(t) or ρ_a(t). The profile must include high-frequency motions that cannot be modeled by the  , , and quadratic polynomial terms in Equations (1a)-(2b); otherwise the detection test will lose all of its power. A motion profile equal to one complete period of a sine wave has the needed richness.

Suppose one starts with a data batch that is comprised of carrier-phase time histories for L different GNSS satellites:  for samples k = 1, …, M_j and for satellites j = 1,…, L. A standard hypothesis test develops two probability density functions for these data, one conditioned on the null hypothesis of no spoofing, H₀, and the other conditioned on the hypothesis of spoofing, H₁.  The Neyman-Pearson lemma (see Further Reading) proves that the optimal hypothesis test statistic equals the ratio of these two probability densities. Unfortunately, the required probability densities depend on additional unknown quantities. In the 1-D motion case, these unknowns include the , , and coefficients, the dot product , and the direction   if one assumes that the UE attitude is unknown. A true Neyman-Pearson test would hypothesize a priori distributions for these unknown quantities and integrate their dependencies out of the two joint probability distributions. Our sub-optimum test optimally estimates relevant unknowns for each hypothesis based on the carrier-phase data, and it uses these estimates in the Neyman-Pearson probability density ratio. Although sub-optimal as a hypothesis test, this approach is usually effective, and it is easier to implement than the integration approach in the present case.

Consider the case of 1-D antenna articulation and unknown UE attitude. Maximum-likelihood calculations optimally estimate the nuisance parameters  , , and   for j = 1, …, L for both hypotheses along with the unit vector for the non-spoofed hypothesis, or the scalar dot product  for the spoofed hypothesis. The estimation calculations for each hypothesis minimize the negative natural logarithm of the corresponding conditional probability density. Because  , , and enter the resulting cost functions quadratically, their optimized values can be computed as functions of the other unknowns, and they can be substituted back into the costs. This part of the calculation amounts to a batch high-pass filter of both the antenna motion and the carrier-phase response.

The remaining optimization problems take, under the non-spoofed hypothesis, the form:

find:          (3a)

to minimize:         (3b)

subject to:                (3c)

and, under the spoofed hypothesis, the form:

find:      η    (4a)

to minimize:         (4b)

subject to:      .   (4c)

The coefficient  is a function of the deflections  for k = 1, …, M_j, and the non-homogenous term  is derived from the jth phase time history  for k = 1, …, M_j. These two quantities are calculated during the  , , optimization. The constraint in Equation (3c) forces the estimate of the antenna articulation direction to be unit-normalized. The constraint in Eq. (4c) ensures that η is a physically reasonable dot product.

The optimization problems in Equations (3a)-(3c) and (4a)-(4c) can be solved in closed form using techniques from the literature on constrained optimization, linear algebra, and matrix factorization. The optimal estimates of  and η can be used to define a spoofing detection statistic that equals the natural logarithm of the Neyman-Pearson ratio:

(5)

It is readily apparent that γ constitutes a reasonable test statistic: If the signal is being spoofed so that carrier-phase sameness is the best model, then η_opt will produce a small value of  because the spoofed-case cost function in Equation (4b) is consistent with carrier-phase sameness. The value of , however, will not be small because the plurality of   directions in Equation (3b) precludes the possibility that any  estimate will yield a small non-spoofed cost. Therefore, γ will tend to be a large negative number in the event of spoofing because  >>  is likely. In the non-spoofed case, the opposite holds true:   will yield a small value of , but no estimate of η will yield a small , and γ will be a large positive number because  << .

Therefore, a sensible spoofing detection test employs a detection threshold γ_th somewhere in the neighborhood of zero. The detection test computes a γ value based on the carrier-phase data, the antenna articulation time history, and the calculations in Equations (3a)-(5). It compares this γ to γ_th. If γ ≥ γ_th, then the test indicates that there is no spoofing. If γ < γ_th, then a spoofing alert is issued.

The exact choice of γ_th is guided by an analysis of the probability of false alarm. A false alarm occurs if a spoofing attack is declared when there is no spoofing. The false-alarm probability is determined as a function of γ_th by developing a γ probability density function under the null hypothesis of no spoofing p(γ|H₀). The probability of false alarm equals the integral of p(γ|H₀) from γ =  to γ = γ_th. This integral relationship can be inverted to determine the γ_th threshold that yields a given prescribed false-alarm probability

A complication arises because p(γ|H₀) depends on unknown parameters,   in the case of an unknown UE attitude and 1-D antenna motion. Although sub-optimal, a reasonable way to deal with the dependence of p(γ|,H₀) on  is to use the worst-case  for a given γ_th. The worst-case articulation direction  maximizes the p(γ|,H₀) false-alarm integral. It can be calculated by solving an optimization problem. This analysis can be inverted to pick γ_th so that the worst-case probability of false alarm equals some prescribed value. For most actual  values, the probability of false alarm will be lower than the prescribed worst case.

Given γ_th, the final needed analysis is to determine the probability of missed detection. This analysis uses the probability density function of g under the spoofed hypothesis, p(γ|η,H₁). The probability of missed detection is the integral of this function from γ = γ_th to γ = +. The dependence of p(γ|η,H₁) on the unknown dot product η can be handled effectively, though sub-optimally, by determining the worst-case probability of false alarm. This involves an optimization calculation, which finds the worst-case dot product η_wc that maximizes the missed-detection probability integral. Again, most actual η values will yield lower probabilities of missed detection.

Note that the above-described analyses rely on approximations of the probability density functions p(γ|,H₀) and p(γ|η,H₁). The best approximations include dominant Gaussian terms plus small chi-squared or non-central chi-squared terms. It is difficult to analyze the chi-squared terms rigorously. Their smallness, however, makes the use of Gaussian approximations reasonable.

We have developed and evaluated several alternative formulations of this spoofing detection method. One is the case of full 3-D b_a(t) antenna motion with unknown UE attitude. The full direction cosines matrix A is estimated in the modified version of the non-spoofed optimal fit calculations of Equations (3a)-(3c), and the full spoofing direction vector  is estimated in the modified version of Equations (4a)-(4c). A different alternative allows the 1-D motion time history ρ_a(t) to have an unknown amplitude-scaling factor that must be estimated. This might be appropriate for a UAV drone with a wing-tip-mounted antenna if it induced antenna motions by dithering its ailerons. In fixed-based applications, as might be used by a financial institution, a cell-phone tower, or a power-grid monitor, the attitude would be known, which would eliminate the need to estimate  or A for the non-spoofed case.

Test Results

The initial tests of our concept involved generation of simulated truth-model carrier-phase data  using simulated , , and polynomial coefficients, simulated satellite LOS direction vectors  for the non-spoofed cases, a simulated true spoofer LOS direction  for the spoofed cases, and simulated antenna motions parameterized by  and ρ_a(t). Monte-Carlo analysis was used to generate many different batches of phase data with different random phase noise realizations in order to produce simulated histograms of the p(γ|, H₀) and p(γ|η,H₁) probability density functions  that are used in false-alarm and missed-detection analyses.

The truth-model simulations verified that the system is practical. A representative calculation used one cycle of an 8-Hz 1-D sinusoidal antenna oscillation with a peak-to-peak amplitude of 4.76 centimeters (exactly 1/4 of the L1 wavelength). The accumulation frequency was 1 kHz so that there were M_j = 125 carrier-phase measurements per satellite per data batch. The number of satellites was L = 6, their  LOS vectors were distributed to yield a geometrical dilution of precision of 3.5, and their carrier-to-noise-density ratios spanned the range 38.2 to 44.0 dB-Hz. The worst-case probability of a spoofing false alarm was set at 10^-5 and the corresponding worst-case probability of missed detection was 1.2 ´ 10^-5. Representative non-worst-case probabilities of false alarm and missed detection were, respectively, 1.7 ´ 10^-9 and 1.1 ´ 10^-6. These small numbers indicate that this is a very powerful test. Ten-thousand run Monte-Carlo simulations of the spoofed and non-spoofed cases verified the reasonableness of these probabilities and the reasonableness of the p(γ|, H₀) and p(γ|η,H₁) Gaussian approximations that had been used to derive them.

The live-signal tests bore out the truth-model simulation results. The only surprise in the live-signal tests was the presence of significant multipath, which was evidenced by received carrier amplitude oscillations that correlated with the antenna oscillations and whose amplitudes and phases varied among the different received GPS signals. As a verification that these oscillations were caused by multipath, the only live-signal data set without such amplitude oscillations was the one taken in the NASA Wallops anechoic chamber, where one would not expect to find multipath. The multipath, however, seems to have negligible impact on the efficacy of this spoofing detection system.

FIGURES 5 and 6 show the results of typical non-spoofed and spoofed cases from WSMR live-signal tests that took place on the evening of June 19–20, 2012. Each plot shows the spoofing detection statistic γ on the horizontal axis and various related probability density functions on the vertical axis. This statistic has been calculated using a modified test that includes the estimation of two additional unknowns: an antenna articulation scale factor f and a timing bias t₀ for the decaying sinusoidal oscillation . The damping ratio ζ and the undamped natural frequency w_n are known from prior system identification tests.

Figure 5. Spoofing detection statistic, threshold, and related probability density functions for a typical non-spoofed case with live data.

Figure 6. Performance of a typical spoofed case with live data: spoofing detection statistic, threshold, and related probability density functions.

The vertical dashed black line in each plot shows the actual value of γ as computed from the GPS data. There are three vertical dash-dotted magenta lines that lie almost on top of each other. They show the worst-case threshold values γ_th as computed for the optimal and ±2σ estimates of t₀: t_0opt, t_0opt+2σ_t0opt, and t_0opt-2σ_t0opt. They have been calculated for a worst-case probability of false alarm equal to 10^-6. An ad hoc method of compensating for the prototype system’s t₀ uncertainty is to use the left-most vertical magenta line as the detection threshold γ_th. The vertical dashed black line lies very far to the right of all three vertical dash-dotted magenta lines in Figure 5, which indicates a successful determination that the signals are not being spoofed. In Figure 6, the situation is reversed. The vertical dashed black line lies well to the left of the three vertical dash-dotted magenta lines, and spoofing is correctly and convincingly detected.

These two figures also plot various relevant probability density functions. Consistent with the consideration of three possible values of the t₀ motion timing estimate, these are plotted in triplets. The three dotted cyan probability density functions represent the worst-case non-spoofed situation, and the dash-dotted red probability functions represent the corresponding worst-case spoofed situations. Obviously, there is sufficient separation between these sets of probability density functions to yield a powerful detection test, as evidenced by the ability to draw the dash-dotted magenta detection thresholds in a way that clearly separates the red and cyan distributions. Further confirmation of good detection power is provided by the low worst-case probabilities of false alarm and missed detection, the latter metric being 1.6 ´ 10^-6 for the test in Figure 5 and 7 ´ 10^-8 for Figure 6.

The solid-blue distributions on the two plots correspond to the η_opt estimate and the spoofed assumption, which is somewhat meaningless for Figure 5, but meaningful for Figure 6. The dashed-green distributions are for the  estimate under the non-spoofed assumption. The wide separations between the blue distributions and the green distributions in both figures clearly indicate that the worst-case false-alarm and missed-detection probabilities can be very conservative.

The detection test results in Figures 5 and 6 have been generated using the last full oscillation of the respective carrier-phase data, as in Figures 3 and 4, but applied to different data sets. In Figure 3, the last full oscillation starts at t = 3.43 seconds, and it starts at t = 2.11 seconds in Figure 4. The peak-to-peak amplitude of each last full oscillation ranged from 4-6 centimeters, and their periods were shorter than 0.5 seconds. It would have been possible to perform the detections using even shorter data spans had the mechanical oscillation frequency of the cantilevered antenna been higher.

Conclusions

In this article, we have presented a new method to detect spoofing of GNSS signals. It exploits the effects of intentional high-frequency antenna motion on the measured beat carrier phases of multiple GNSS signals. After detrending using a high-pass filter, the beat carrier-phase variations can be matched to models of the expected effects of the motion. The non-spoofed model predicts differing effects of the antenna motion for the different satellites, but the spoofed case yields identical effects due to a geometry in which all of the false signals originate from a single spoofer transmission antenna. Precise spoofing detection hypothesis tests have been developed by comparing the two models’ ability to fit the measured data.

This new GNSS spoofing detection technique has been evaluated using both Monte-Carlo simulation and live data. Its hypothesis test yields theoretical false-alarm probabilities and missed-detection probabilities on the order of 10^-5 or lower when working with typical numbers and geometries of available GPS signals and typical patch-antenna signal strengths. The required antenna articulation deflections are modest, on the order of 4-6 centimeters peak-to-peak, and detection intervals less than 0.5 seconds can suffice.

A set of live-signal tests at WSMR evaluated the new technique against a sophisticated receiver/spoofer, one that mimics all visible signals in a way that foils standard RAIM techniques. The new system correctly detected all of the attacks. These are the first known practical detections of live-signal attacks mounted against a civilian GNSS receiver by a dangerous new generation of spoofers.

Future Directions

This work represents one step in an on-going “Blue Team” effort to develop better defenses against new classes of GNSS spoofers. Planned future improvements include 1) the ability to use electronically synthesized antenna motion that eliminates the need for moving parts, 2) the re-acquisition of true signals after detection of spoofing, 3) the implementation of real-time prototypes using software radio techniques, and 4) the consideration of “Red-Team” counter-measures to this defense  and how the “Blue Team” could combat them; counter-measures such as high-frequency phase dithering of the spoofed signals or coordinated spoofing transmissions from multiple locations.

Acknowledgments

The authors thank the following people and organizations for their contributions to this effort:  The NASA Wallops Flight Facility provided access to their anechoic chamber. Robert Miceli, a Cornell graduate student, helped with data collection at that facility. Dr. John Merrill and the Department of Homeland Security arranged the live-signal spoofing tests. The U.S. Air Force 746th Test Squadron hosted the live-signal spoofing tests at White Sands Missile Range. Prof. Todd Humphreys and members of his University of Texas at Austin Radionavigation Laboratory provided live-signal spoofing broadcasts from their latest receiver/spoofer.

Manufacturers

The prototype spoofing detection data capture system used an Antcom Corp. (www.antcom.com) 2G1215A L1/L2 GPS antenna. It was connected to an Ettus Research (www.ettus.com) USRP (Universal Software Radio Peripheral) N200 that was equipped with the DBSRX2 daughterboard.

MARK L. PSIAKI is a professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, Ithaca, New York. He received a B.A. in physics and M.A. and Ph.D. degrees in mechanical and aerospace engineering from Princeton University, Princeton, New Jersey. His research interests are in the areas of GNSS technology, applications, and integrity, spacecraft attitude and orbit determination, and general estimation, filtering, and detection.

STEVEN P. POWELL is a senior engineer with the GPS and Ionospheric Studies Research Group in the Department of Electrical and Computer Engineering at Cornell University. He has M.S. and B.S. degrees in electrical engineering from Cornell University. He has been involved with the design, fabrication, testing, and launch activities of many scientific experiments that have flown on high altitude balloons, sounding rockets, and small satellites. He has designed ground-based and space-based custom GPS receiving systems primarily for scientific applications.

BRADY W. O’HANLON is a graduate student in the School of Electrical and Computer Engineering at Cornell University. He received a B.S. in electrical and computer engineering from Cornell University. His interests are in the areas of GNSS technology and applications, GNSS security, and GNSS as a tool for space weather research.

VIDEO

Here is a video (in m4v format) of Cornell University’s antenna articulation system for the team’s first prototype spoofing detector tests.

FURTHER READING

• The Spoofing Threat and RAIM-Resistant Spoofers

“Status of Signal Authentication Activities within the GNSS Authentication and User Protection System Simulator (GAUPSS) Project” by O. Pozzobon, C. Sarto, A. Dalla Chiara, A. Pozzobon, G. Gamba, M. Crisci, and R.T. Ioannides, in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of The Institute of Navigation, Nashville, Tennessee, September 18–21, 2012, pp. 2894-2900.

“Assessing the Spoofing Threat” by T.E. Humphreys, P.M. Kintner, Jr., M.L. Psiaki, B.M. Ledvina, and B.W. O’Hanlon in GPS World, Vol. 20, No. 1, January 2009, pp. 28-38.

Vulnerability Assessment of the Transportation Infrastructure Relying on the Global Positioning System – Final Report. John A. Volpe National Transportation Systems Center, Cambridge, Massachusetts, August 29, 2001.

• Moving-Antenna and Multi-Antenna Spoofing Detection

“Robust Joint Multi-Antenna Spoofing Detection and Attitude Estimation by Direction Assisted Multiple Hypotheses RAIM” by M. Meurer, A. Konovaltsev, M. Cuntz, and C. Hattich, in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of The Institute of Navigation, Nashville, Tennessee, September 18–21, 2012, pp. 3007-3016.

“GNSS Spoofing Detection for Single Antenna Handheld Receivers” by J. Nielsen, A. Broumandan, and G. Lachapelle in Navigation, Vol. 58, No. 4, Winter 2011, pp. 335-344.

• Alternate Spoofing Detection Strategies

“Who’s Afraid of the Spoofer? GPS/GNSS Spoofing Detection via Automatic Gain Control (AGC)” by D.M. Akos, in Navigation, Vol. 59, No. 4, Winter 2012-2013, pp. 281-290.

“Civilian GPS Spoofing Detection based on Dual-Receiver Correlation of Military Signals” by M.L. Psiaki, B.W. O’Hanlon, J.A. Bhatti, D.P. Shepard, and T.E. Humphreys in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of The Institute of Navigation, Portland, Oregon, September 19–23, 2011, pp. 2619-2645.

• Statistical Hypothesis Testing

Fundamentals of Statistical Signal Processing, Volume II: Detection Theory by S. Kay, published by Prentice Hall, Upper Saddle River, New Jersey,1998.

An Introduction to Signal Detection and Estimation by H.V. Poor, 2nd edition, published by Springer-Verlag, New York, 1994.

Video (in m4v format) of Cornell University’s antenna articulation system for their first prototype spoofing detector tests.

When a team of firefighters, first responders, or soldiers operates inside a building, in urban canyons, underground, in foliage, or under the forest canopy, the GPS-denied environment presents unique navigation challenges. An enhanced personal inertial navigation system (ePINS), based on a strapdown navigation solution using a mid-grade IMU and wavelet-based motion-classification algorithms, can track positions with errors of less than 2 percent of distance traveled in both indoor and outdoor environments.

By Yunqian Ma, Wayne Soehren, Wes Hawkinson, and Justin Syrstad

Numerous pedestrian navigation applications are currently available or proposed for development. Some of them include localization for coordinating firefighters, first responders, or soldiers. In these applications, the safety and efficiency of the entire team relies directly on the location and orientation of each team member. Operations in high signal interference areas such as cities, rugged terrain, forest, or indoor spaces deliver intermittent or no GPS signal. An alternative to GPS-based location is required.

In this article, we introduce an enhanced personal inertial navigation system (ePINS) solution specifically designed for environments where GPS is unavailable. ePINS combines an array of state-of-the-art sensors and fusion algorithms into a personal navigation system that provides accurate location information for pedestrian applications.

The ePINS concept.

The ePINS solution has the following benefits:

• Accurate positioning in GPS-denied environments;
• Small, lightweight unit can be easily carried by first responders, rescue workers, or soldiers;
• Ruggedized packaging to withstand difficult first responder and military environments.

Features of  the ePINS unit include:

• State-of-the-art micro-electromechanical systems (MEMS) gyros and accelerometers, barometric altitude sensor, and advanced navigation software;
• Advanced motion classification algorithms that accurately identify and measure user activity;
• Immunity to magnetic disturbances.

Related Work

In the field of personal navigation, it is common to find systems that rely on sensors that need infrastructure (for example, Wi-Fi positioning) or sensors that actively emit electro-magnetic radiation (such as Doppler radar). These requirements are major drawbacks for communities such as dismounted soldiers in hostile environments.

Other approaches exploit the so-called Zero-velocity update (ZUPT) mechanism, which resets the inertial measurement unit (IMU) velocity errors during the stationary phase of motion. However, implementation of such schemes relies on sensors embedded in footwear, which is not readily accepted in many user communities.

To address these drawbacks, Honeywell has been developing advanced aiding techniques for personal navigation that do not rely on infrastructure and compute a self-contained, relative-navigation solution based only on passive sensors. One technique that Honeywell has developed uses displacement estimation from human-motion models. This technology has been implemented in the ePINS prototype and shows promising performance.

The human-motion model uses IMU measurements as inputs and was developed to infer distance traveled. It generates a displacement estimate that is used as a measurement in the navigation filtering process. The first version of this model was matured under the DARPA individual Precision Inertial Navigation System (iPINS) program. The iPINS system used an IMU, GPS, barometer, and motion classification to estimate a person’s position in both indoor and outdoor environments. In this system, IMU signal characteristics (e.g., peaks and valleys in the accelerations induced by walking) were exploited to differentiate between walking and running. Honeywell recently expanded the human-motion model to identify more specific motion types using a new wavelet motion classification method.

System Description

Figure 1 displays the hardware architecture of the ePINS, a small battery-powered, highly integrated electronic system. The ePINS processing platform is an ARM11-based, i.MX31 system-on-module, paired with support electronics. In addition to the processing platform, the ePINS assembly includes a MEMS IMU, a barometric pressure sensor, a digital magnetometer, and a GPS receiver.

Figure 1. ePINS hardware architecture.

The MEMS IMU provides inertial measurements for strapdown navigation. The IMU’s small package size, light weight, low power consumption, and impressive performance make it attractive for use in the ePINS system. The device is less than 5 cubic inches and weighs less than 0.35 pounds. It consumes about 3 watts of power with a typical current draw of 600mA at 5V.

The ePINS software system is shown in Figure 2. The navigation software runs within Honeywell’s Embedded Computing Toolbox and Operating System (ECTOS IIc), which provides a layered, customizable, and reusable software architecture for implementing navigation, guidance, and control software. A Honeywell-developed simulation tool for offline analysis and development of ECTOS-based software was also used in ePINS development and testing.

Figure 2. ECTOS IIc hierarchical software structure.

The ePINS demonstration device can achieve path performance of less 2 percent distance traveled for walking motion after 1 hour of operation, independent of the magnetic environment. Current performance, packaging characteristics, and interfaces are summarized in Table 1.

Table 1. ePINS performance objectives and physical specifications.

Algorithm Description

Figure 3 depicts the overall sensor integration and data processing scheme used in the ePINS device.

Figure 3. Sensor integration using the ECTOS extended Kalman filter.

Extended Kalman Filter (EKF).  The EKF estimates the navigation and sensor errors and computes the resets applied to the strapdown navigation solution to increase its accuracy. Error models for the navigation sensors (IMU, barometric altimeter, magnetometer, GPS, and motion classification) are contained in the EKF. For the ePINS device, the virtual measurements from the step-length model and the strapdown navigation solution are fused by the EKF to assist in bounding the time dependent error growth of the strapdown navigator, which in turn helps maintain calibration of the inertial sensors. A key output of the EKF is the navigation confidence, which is an estimate of the accuracy of the navigation solution.

An important aspect of the EKF and step-length modeling is the residual test that the EKF supports. This test provides a reasonableness comparison between the step-length model estimate and the distance predicted by the strapdown navigation system. This capability significantly increases the robustness of the navigation solution, especially when the user is engaged in motions not recognized during motion classification.

Human-Motion Model. The human-motion model includes two components: wavelet motion classification and step-length model estimation. The wavelet motion classification identifies the type of motion the user is performing, and the step-length model acts as a virtual sensor that quantifies the motion as a distance-traveled estimate.

Wavelet Motion Classification. Human motions are very diverse and highly irregular. Determining what motion is being performed is a challenging problem of classification. Honeywell’s solution is based on wavelet transformation of IMU data. Predefined, or known, characteristics of a variety of motions (such as walking, running, crawling, etc.) are cataloged and stored to a device’s memory. Estimates of those same characteristics for a user are then computed in real time and compared to the catalog of stored information to find the best match.

Generating the catalog of stored information is an offline task that begins by “segmenting” recorded IMU time domain data into individual steps. An example of the output of the segmentation process is shown in Figure 4.

Figure 4. Segmentation of the IMU data using the y-axis accelerometer signal.

Figure 5 displays the segmentation results for two different walking styles (in red and blue) across approximately 15 example steps. As is evident from the graph, walking has characteristics that are common across users, for example, the sharp peaks in the z-axis acceleration caused by foot-ground impacts. Once the data has been segmented, a wavelet transformation on each data channel is performed. Wavelet transformation for many users over many different motion types takes place offline. Subsequently, a wavelet descriptor is built for each motion type based on the transformations into the wavelet domain. With this method, a wide variety of information (that is, descriptors) suitable for input to a classifier is captured about each motion. These descriptors are then cataloged and stored in memory on the ePINS device.

Figure 5. Sample steps for two subjects (red) and (blue).

Finally, for the online phase, the wavelet descriptor of the incoming IMU data is calculated by performing a wavelet transformation on each data channel. This descriptor is then compared to the pre-computed and stored descriptors to classify the motion. FIGURE 7 shows an example of the motion classifier output, where a running motion was used as an input. The classifier successfully determined the motion type (blue field), frequency and phase of the input motion, depicted by the tallest rectangle in the figure.

Figure 7. Classification results from a query of running at a certain frequency and phase (depicted by the dark sphere).

Step-Length Modeling. Once the current motion is identified, a step-length model specific to that motion is used to aid the navigation algorithms. The model for each motion type is obtained by first collecting data that measures step length and step frequency. From this data, the step-length models can be computed by performing a regression analysis of the step-length vs. step-frequency data. Since the step-length models act as a virtual sensor, the models must be as accurate as possible to achieve better system performance. To attain model accuracy, an accurate data collection method is needed.

For ePINS development, step-length models for multiple users have been identified from step-length and timing information using a precise GPS truth reference system. Step-length regression calculations then determine the step length as a function of step frequency (that is, inverse of the step time period).  An example of GPS truth data and the corresponding regression model are shown in FIGURE 6 for walking motions.

Figure 6. Step length versus frequency for the walking of subject.

Although basic step-length models are created offline, online calibration of the step-length model can be performed by the EKF if GPS is available during operation. Online calibration tends to increase the overall position accuracy, as variations in the step-length models are likely due to slight variations in biometric differences across humans, terrain features, and even mission plans and duration.

Heading Determination. Heading initialization is one of the key concerns during system start up. In its current operational use, the ePINS device may perform a dynamic or a static initialization of heading. The static method requires the user to survey the system’s initial heading to an accuracy value that is usually specified by mission performance objectives; the absolute position accuracy is dependent upon the accuracy of the initial heading.

The dynamic method is a general method for heading initialization; it is performed without input from the user, but is possible only when GPS is available. This method of heading initialization does not use any a priori information about heading and requires an EKF implementation with a large-azimuth error model. This method requires an additional period of time in which the heading error uncertainty converges.

User Interface. During a mission, the user can interact with the navigation system and monitor its output on a display. The current ePINS prototype offers two-way communication via a serial connection. The serial communication is made wireless by the addition of a Bluetooth interface. Users can use this link to monitor the status of the navigation solution and to send commands to the device.

Honeywell has developed an application for the Android platform for this purpose. One of the key features of the interface design is that the navigation system outputs data in a standard NEMA format. Thus, publically available Android applications, not just proprietary applications, can also receive and display the navigation solution output by the ePINS device.

Honeywell’s personal navigation application displays the user’s traveled trajectory in real-time. The application can be adapted to include building floor plans as well as other navigation information.

Results

The ePINS prototype has been evaluated both in simulations and indoor/outdoor experiments. The navigation results presented here were obtained in February 2012 at a Honeywell facility (FIGURE 8). First, the user completed the heading calibration, and then online step parameter estimation in the presence of GPS was performed. Once calibration and training was completed, the GPS was disabled to simulate a GPS-denied environment outdoors. The user than transitioned to indoors (with GPS still disabled), and walked a course inside that included walking up and down stairs (FIGURE 9) and ended in a conference room (FIGURE 10).

Figure 8. Course for the Honeywell facility demonstration.

Figure 9. The user walking up stairs.

Figure 10. The user at the end of the demo.

Over these conditions, the ePINS system performed robustly and within performance specifications. Live demonstrations and testing showing similar levels of performance were performed at the 2012 Joint Navigation Conference (JNC) and at military test sites in California and Indiana.

Summary

The technical approach of the ePINS solution to the problem of personnel navigation in GPS-denied environments is based on a strapdown navigation solution maintained using a mid-grade IMU and advanced motion-classification algorithms. We integrated an array of sensors and software into a system that provides accurate position information and is suitable for use by first responders, soldiers, and other personnel where GPS is unavailable. ePINS works well for a variety of pedestrian motion types, including walking, running, crawling, walking upstairs, walking downstairs, sidestepping, and walking backwards. The motion classification and modeling method is extensible to other motion types.

We tested the ePINS system in indoor and outdoor environments. FIGURE 11 depicts the future ePINS concept, and TABLE 2 presents its future physical characteristics.

Figure 11. Future ePINS concept and mounting position.

Table 2. Packaging characteristics of the future ePINS.

Acknowledgments

This article is based on a presentation made at ION GNSS 2012.

Manufacturers

The ePINS processing platform uses Honeywell Agile Navigation and Guidance Integrated Electronics support electronics. It includes a Honeywell HG1930 MEMS IMU, a Bosch Sensortec BMP085 barometric pressure sensor, a Honeywell HMC6343 digital magnetometer, and a NovAtel OEMStar GPS receiver.

Yunqian Ma is a principal scientist at Honeywell Aerospace. He received his Ph.D. degree in electrical engineering from the University of Minnesota, Twin Cities. He is currently the program manager of the GPS-denied navigation program and the next-generation personal navigation program.

Wayne Soehren is a senior technical manager at Honeywell Aerospace. He was the program manager for the development of Honeywell’s first MEMS-based GPS/INS, which developed the core capability now used in Honeywell’s IGS-2XX family of MEMS-based GPS/INS products. He holds an MSEE from the University of Minnesota.

Wes Hawkinson is an engineering fellow at Honeywell Aerospace. He holds a BSEE/CE from the University of Wisconsin–Madison.
Justin Syrstad is a guidance and navigation scientist. He received a master’s degree in aerospace engineering from the University of Minnesota.

This first FOC satellite is functionally identical to the first four in-orbit validation (IOV) satellites already in orbit, but has been built by a separate industrial team. Like the other 21 FOC satellites so far procured by ESA, the satellite’s prime contractor is OHB System AG, and the navigation payload was produced by Surrey Satellite Technology Ltd. in Guildford, UK.

Thermal vacuum testing at the European Space Research and Technology Centre (ESTEC) will simulate temperature extremes the satellites must endure in the airlessness of space throughout their 12-year working lifetimes. Without any moderating atmosphere, temperatures can shift hundreds of degrees from sunlight to shadow.

Other activities on the schedule include shaker and acoustic noise testing — simulating the vibration and noise of launch — as well as electromagnetic compatibility and antenna testing, placing the satellite in chambers shielded from all external radio signals to reproduce infinite space and check that its various antennas and electrical systems are interoperable without harmful interference.

“The Galileo FOC satellites provide the same capabilities as the previous IOV satellites, but with improved performance, such as higher transmit power,” explained Giuliano Gatti, the head of the Galileo Space Segment Procurement Office. “They are to all intents a new design that requires a full checkout before getting the green light for launch.”

The second FOC flight model is due to arrive at ESTEC in early June, and the third in the middle of July. The first two satellites are to be placed in orbit on board a Soyuz launcher, with a scheduled lift-off from Kourou in French Guyana this fall, with two more due to follow by the end of the year.

The first four Galileo IOV satellites, launched in 2011 and 2012, were provided by EADS Astrium with Thales Alenia Space Italy responsible for integrating the satellites and Astrium in Portsmouth, UK, providing the navigation payloads. They provided their first navigation fix in March 2013.

The definition, development and in-orbit validation phases of the Galileo programme are being carried out by ESA and co-funded with the European Commission (EC).

The subsequent FOC phase is managed and funded by the EC. The commission has delegated the role of design and procurement agent to ESA for the FOC phase. At the same time as the satellites are being assembled on a production-line basis, ground stations are also being established on European territories around the globe.

Photo credit: Pat Corkery, United Launch Alliance.

GPS Leaves This Earth

A t 5:38 p.m. Eastern Daylight Time (21:38 UTC) on May 15,  the fourth GPS IIF satellite, Space Vehicle Number (SVN) 66 built by Boeing, ascended towards orbit aboard a United Launch Alliance Atlas V rocket at from Cape Canaveral Air Force Station, Florida.

“The GPS constellation remains healthy and continues to meet and exceed the performance standards to which the satellites were built. Our goal is to deliver sustained, reliable GPS capabilities to America’s warfighters, our allies, and civil users around the world, and this is done by maintaining GPS performance, fielding new capabilities and developing more robust modernized capabilities for the future,” said Colonel Bernie Gruber, director of the U.S. Air Force Space and Missile Systems Center’s GPS Directorate.

The new capabilities of the IIF satellites will provide greater navigational accuracy through improvements in atomic clock technology; a more robust signal for commercial aviation and safety-of-life applications, known as the new third civil signal (L5); and a 12-year design life providing long-term service. These upgrades deliver improved anti-jam capabilities for warfighters and improved security for military and civil users around the world, the Air Force said in a statement.

The IIF-4 satellite is expected to complete testing in August, after which it will be utilized as a reserve or backup satellite. It becomes the fourth satellite in a 12-strong network of GPS IIF spacecraft manufactured by Boeing as lead contractor, the first of which was boosted into orbit in May 2010. The Air Force expects the first of the next-generation GPS IIIA satellites to enter service sometime in 2014.

System Briefs

GLONASS. The GLONASS 747 M-series satellite launched on April 26 has maneuvered into an orbital slot near GLONASS 728, the operational satellite in Plane 1, slot 2. 747 will presumably serve as a reserve until it replaces 728, unless another Plane 1 satellite expires first. The next Russian launch, a GLONASS-M trio, is scheduled for July 1. There are currently 24 operational GLONASS satellites.

IRNSS. The first Indian Regional Navigation Satellite System satellite is expected to rise at the end of June. The IRNSS plans to orbit of seven: three geostationary and four geosynchronous, providing regional coverage via navigation signals in the L5 and S bands.