The Paveway GBU-50.

Raytheon Company has completed delivery of more than 200 Paveway GBU-50 guidance kits to a European partner.

The GBU-50 provides the 2,000-pound MK-84 or the BLU-109 penetrator with all-weather GPS navigation combined with precision terminal laser guidance. A full range of selectable terminal impact angles combined with a mature combat-proven, height-of-burst maximizes the capabilities of both the MK-84 and BLU-109.

“This delivery is a significant milestone for the Enhanced Paveway II program as it provides unique capabilities to our allies,” said Harry Schulte, vice president of Raytheon Missile Systems’ Air Warfare Systems. “As we begin our second production run of the GBU-50, we have substantial interest from the international community.”

Each Enhanced Paveway II guidance and control section is compatible with warheads ranging from the 250-pound MK-81 to the 2,000-pound MK-84 along with the BLU-109. There is no need for the warfighter to acquire a different guidance and control section for different warhead use.

According to Raytheon, the Paveway family of laser-guided and GPS and laser-guided bombs, has revolutionized tactical air-to-ground warfare by converting “dumb” bombs into precision-guided munitions. Paveway laser-guided bomb kits comprised more than half the air-to-ground precision-guided weapons used in Operation Iraqi Freedom, Enduring Freedom and Unified Protector.

In a recent USAF technical report, the need for a new non-GPS based positioning capability was described by the 746th Test Squadron as the key component for “the realization of the new ‘gold standard truth system’ for the increasingly demanding test and evaluation of future navigation systems for the U.S. Department of Defense.” Locata is the new technology now contracted to enable this capability for the USAF’s future truth reference, the Ultra High-Accuracy Reference System (UHARS).

The report documented extensive testing of Locata’s new capabilities when a LocataNet covering 1,350 square miles (3,500 square kms) was first deployed at White Sands. The USAF and the 746th Test Squadron proved a LocataNet can accurately position USAF aircraft over a large area when GPS is denied. Locata delivered accurate independent positioning as good as, or better than, the USAF’s current CIGTF Reference System (CRS). The Locata non-GPS based positioning capability is core to the UHARS that will now replace the CRS in 2014.

After the exhaustive aircraft testing, the USAF concluded that the Locata system had not only met the extremely demanding contractual tracking and positioning requirements, but actually exceeded them on many points. Some of the milestones documented and confirmed by the USAF included:

• The USAF report documented LocataNet position accuracy of 2.5 inches (6cm) horizontally and 6 inches (15 cm) vertically – about the size of a dollar bill – for aircraft flying at a distance of 30 miles (50km) at up to 350 mph (550 km/hr) at 25,000 feet, without GPS.
• Throughout the period of the testing, the entire White Sands network achieved nanosecond-accurate synchronization within several minutes of the LocataNet being activated, and remained synchronized even during severe weather until turned off at the end of each test.
• The USAF tests showed that a stock standard Locata transmitter – the same unit used in commercial applications like mining – could have an amplifier attached to easily boost signals for long-range reception. By attaching a simple, inexpensive 10 watt amplifier, the USAF proved that Locata signals could be acquired and tracked by aircraft at distances of up to 60 miles (100 km). Longer distances could be enabled by attaching higher-powered amplifiers.
• Before to the White Sands flight trials, commercial Locata systems had only been used to position ground-based vehicles, such as cars, trucks, bulldozers and drill rigs in local areas. For the USAF tests, however, the Locata system needed to function under dynamic aircraft operating maneuvers, including banking, angular and linear accelerations, airspeeds up to 300 knots (560 km/hr), and altitudes up to 30,000 feet above sea level. The required aircraft performance was verified in the real-world testing.
• The USAF required Locata to design, prototype and then deliver aircraft-certified antennas for use on both the Locata ground-based transmitters and the USAF aircraft. Locata worked with Cooper Antennas Ltd. of Marlow in Buckinghamshire, United Kingdom, to produce an aircraft-certified version of Locata’s new quadrifilar helix antenna design. The Cooper manufactured antennas were used throughout the tests with excellent results, and confirmed Locata’s research and analysis.

“Locata Corp delivered a LocataNet for use in our October 2011 technical demonstration on White Sands Missile Range that provided time and position truth, independent of GPS, that was better than 18 cm (6 inches) per axis while flying at 15,000 and 20,000 foot above mean sea level profiles,” said Christopher Morin, technical director for the 746 Test Squadron. “The solutions provided by the LocataNet were within the accuracy tolerance of the squadron’s CIGTF Reference System and met our threshold objectives. Further analysis has shown that if we optimize the LocataNet deployment, characterize its errors and tightly couple its range and carrier-phase measurements with the other GPS and inertial components on the UHARS pallet into the UHARS solution post-processing software, I am confident we will be able to meet our 5-cm (2-inch) per axis truth reference objective. I am very pleased with the LocataNet’s demonstrated ability to produce an accurate, dynamic truth reference from the relatively static implementation they had already deployed in the mining industry.”

“Locata products developed and sold by important commercial partners like Hexagon and Leica Geosystems have already shown our new technology is a game-changer for positioning over industrial-sized areas,” said Nunzio Gambale, CEO and co-founder of Locata. “However, proving Locata can provide the USAF with centimeter-accurate non-GPS positioning over a vast military area when GPS is jammed instantly elevates our technology achievements into a completely new league. It’s important to grasp the scale of what we’ve done here. The 2,500 square mile LocataNet at White Sands will be 74 times the size of Manhattan Island. It must be clear, our ability to deliver centimeter-level (inch-level) positioning over an area that large, without using GPS satellites, is both unique and totally revolutionary. No one else on Earth can do this. Many valuable industrial and consumer apps will now be built around our amazing inventions, created by Locata’s co-founder David Small and our brilliant engineers.”

“This contract makes it clear you are witnessing the arrival of one of the most important technology developments for the future of the entire positioning industry,” Gambale declared.

Under this new contract Locata will provide the USAF with Locata receivers and LocataLite transmitters to blanket 2,500 square miles (6,500 sq km) of the White Sands Range. Locata will also:

a)     deliver extended hardware warranty, along with ongoing Locata software and firmware upgrades, through to the year 2025;

b)     supply multi-year support for the installation, fielding and testing of Locata networks; and

c)     provide long-term consultation and expert technical advice to ensure optimal operational performance of the USAF’s fielded LocataNet systems.

L-3 Interstate Electronics Corporation (IEC) conducted an operational demonstration of its new TruTrak Evolution (TTE) Type II Selective Availability Anti-Spoofing Module (SAASM) GPS receiver at AUVSI’s Unmanned Systems North America 2012 conference, held last week in Las Vegas. The demonstration highlighted the new TruTrak receiver’s multi-use capabilities as a high-performing Ground-Based GPS Receiver Applications Module (GB-GRAM) for use on UAS platforms and precision weapons.

The TTE offers native Inertial Measurement Unit (IMU) and external oscillator interfaces, user processor, reconfigurable input/output (I/O) and front end, and easy roadmap migration from SAASM to NextGen GPS YMCA modernized technology. Its TTE Type II architecture supports the integration of multiple sensors to simplify all-source navigation solutions for GPS-denied environments. The adaptable architecture allows developers to quickly integrate new sensors without a hardware change, while providing industry-leading core GPS receiver performance and easy migration to NextGen modernized GPS.

“The TTE Type II highlights L-3 IEC’s integrated SAASM/NextGen GPS M-Code roadmap, providing another innovative path in the development of a Common GPS Module,” said Ric Pozo, general manager and vice president of navigation systems at L-3 IEC. “It allows SAASM- based P(Y) and modernized YMCA multichip modules to share a common circuit card assembly, making this a very flexible solution for drop-in GPS receiver replacement and low-risk integration.”

L-3′s TTE Type II provides features required by multiple applications, including a small form factor, high performance, and both passive and active antennas. The TTE Type II adopts the common GB-GRAM Type II electrical and physical interfaces, but with expandable I/O to support a wide range of requirements for ground, air, weapon, and projectile needs.

By Thomas Zysk, Jeffory Luce, and James Cunningham

Augmented reality (AR) is a concept in daily use in the modern technology vernacular. In one popular form, AR enhances football broadcasts with overlaid information such as the first down line. A much more robust capability for application in high-performance navigation systems uses accurate GPS and heading sensors to geographically register a virtual world accurately over a real-world, real-time view. In a military context, AR can provide critical context to situational awareness.

AR for military use was originally developed as a maritime equivalent to the aviator’s heads-up display. Evaluations using a task-load index function showed a 342 percent improvement in side-task operator performance when using AR. Operators do not have to make the mental conversion from 2D (map or chart view) to 3D real-world view. This translation is where errors can be made in high-stress scenarios and forms the root cause of many accidents. AR provides a game-changing capability to enhance warfighter performance when it matters and is invaluable during high-stress, dynamic operations.

Amphibious assault vehicle (AAV), U.S. Marine Corps.

In this navigation context, AR was developed for use in low-visibility situations, such as navigating in dense fog or at night during lights-out missions. The technology can provide a visual depiction of critical points of interest, regardless of real-world visibilities. AR provides the means to integrate sensors and supporting geographic information system and related systems into a cohesive visual display that overcomes environment limitations or such things as closed-hatch operations on military vehicles.

AR delivers two important military capabilities to the warfighter: situational awareness and precision piloting capabilities, both key to survival on the battlefield.

Situational Awareness. Any information with a geographical registration component can be overlaid on the real-world view in a single composite display format. This can track data, threat locations, friendly-force locations, obstacles, and safe havens; the list grows each day. This information adds immensely to the operator’s understanding of the environment. This fused information, over a real-world, real-time view, is functionally an enhanced Common Operational Picture (COP). Operators can be more cognizant of the tactical situation day, night, or in any visibility condition.

Precision Piloting. The faster one drives in an automobile, the further down the road one must focus to stay on the highway. AR provides this look-ahead drive-to-position based on accurate GPS positions. This extends the importance of GPS to high-speed operation or very close maneuvering situations where humans cannot cycle through a chart or map display, then place themselves in the real world to make maneuvering decisions.

AR enables a rich suite of functions supporting the access and maintenance of a COP, and demonstrated maneuver accuracy. For the Augmented Reality Visualization for the Common Operational Picture (ARVCOP) system, any situational awareness information available can be overlaid on the real-world view in a clear and organized way. Operators do not have to go through the process of translating what they see on a map to what they see in front of them, a translation process that often incurs error. AR then delivers this to warfighters through a human-cognition friendly, integrated display of sensor data and geographically registered overlays, as Figure 1 illustrates. The AR view is shown along with a two-dimensional view on the right side of the display.

Figure 1. ARVCOP display example.

Developed by the Office of Naval Research with industry partner Technology Systems Inc., ARVCOP provides a human-machine interface that can magnify the effectiveness of precision positioning. In this article, we discuss how AR is utilized in this context and the results of testing AR precision-navigation systems aboard Marine Corps amphibious assault vehicles (AAVs, see photo) on the beaches of Marine Corps Base Camp Pendleton, California.

Precision piloting, or driving accuracy, is achieved by providing the operator a point toward which to drive that is in relation to the current position. Testing showed that looking ahead or driving to a point forced the operator to self-correct for the effects of wind, waves, and current.

AR is exemplified by a software application that combines real-time video imagery with virtual images to provide a new dimension in navigation piloting accuracy. Figure 2 is an AR display on a ferry boat showing the navigational route marked by rails.

Figure 2. Real world with augmented reality.

AR can overlay critical chart information such as buoys and channel markers, as well as radar or automated information system (AIS) contacts. In fact, any information that has a geo-registration component (geographic location attached) can be precisely overlaid on a real-time or infrared camera view. Operators have reported they are able to maneuver in unfamiliar waters at high speed with confidence, especially at night or in inclement weather (Figure 3).

Figure 3. Obscure visibility with augmented reality.

An operator using AR does not have to look down at a chart, radar, or AIS display, and then up at the real world to put the information into context. Charts, radar, and AIS output 2D information that must be made relevant to a 3D world. Analysis shows that converting 2D to 3D is a strenuous and error-prone task for the brain. Accidents can be caused by an initial mistake, which is then compounded by other decisions made with incorrect information. Figure 4 shows how AR automates the conversion process, allowing the human to focus on other relevant tasks.

Figure 4. Augmented Reality Visualization of the Common Operational Picture (ARVCOP) block diagram.

R & D Hardware

AR applications on AAVs have demonstrated the technology’s utility on land, in water, and through the hazardous surf zone, delivering precise routing through cleared transit lanes. The system is intuitive to operate. Operators with little or no training in AR systems executed precise maneuvers through lanes planned with bends and turns. The AR system used a military GPS and heading device. Electronic chart and tactical data brought positional context to the display. A virtual world was created and software algorithms draped the virtual world over a real-world camera view creating an AR display (Figure 5) for the AAV test.

Figure 5. AAV with research and development commercially available ARVCOP hardware.

Camp Pendleton Tests. In 2009, rigorous testing was completed for the ARVCOP system using AAVs in the surf at Marine Corps Base Camp Pendleton. Safe maneuver lanes were marked with mine-like objects and other hazards. Complex routes that included turns and zigzag patterns were planned toward the beach. Routes were delivered to vehicles using a radio circuit, and adjustments to the planned route were made on the fly to adapt to changing tactical situations.

The AAV is a 26-ton vehicle that is a challenge to operate when placed in a surface environment with wind, waves, and currents. Hardware employed ranged from legacy devices, including a magnetic heading device, to modern devices. With Research and Development (R&D) hardware, the results were dramatic compared to the traditional means of navigating assault lanes. The technology enabled new mission concepts, such as irregular routes ashore and avoidance of hazards sighted by other forces as the mission was in progress. The evaluation criteria for these tests were cross-track errors (CTEs), measured relative to a planned route. Separate, high-accuracy GPS was used for truth data to measure the accuracy of the route driven. Figure 6 shows the video camera and GPS antenna locations on the AAVs.

Figure 6. Video camera is located directly beneath the GPS antenna.

Figure 7 gives an example of the resultant AR video imagery for the R&D commercially available hardware on the AAVs. Figure 8 shows the planned routes for the R&D test evaluations. The distance offshore was 946 meters, and the planned total route length was 1,990 meters.

Figure 7. ARVCOP video using R&D hardware.

Figure 8. Planned route for the R&D testing.

Video Augmentation Accuracy

To determine position accuracy of the augmented figures drawn on the video images, time encoded images were captured. The augmented images were captured by ARVCOP using both the Civilian-Miniature Integrated GPS/INS Tactical System (C-MIGITS III) and the Tactical Navigation Digital Compass System (TACNAV) as input devices. Typically, multiple images are used to determine reference frame biases between the camera and the inertial measurement unit but, in this case, multiple image solutions lacked convergence. For this analysis, single-image solutions were generated. Figure 9, which shows locations of virtual and real objects, is an example of an image used in this analysis. The reference location of the virtual object is the bottom of the green post. The real-object coordinates input to ARVCOP were generated using a GPS survey and have centimeter-level accuracy. Figure 9 illustrates the inaccuracies in the system. During this calibration test, the augmentation showed errors of about 100 mrad (6 degrees) in the display of the virtual objects. (Authors’ note: This paragraph accurately reflects system performance on that day three years ago. Shortly after the test, system modifications were made that eliminated much of that error.)

Figure 9. ARVCOP image captured showing virtual and real objects.

Test Results

Evaluation of the AAV operation using ARVCOP as a driver’s aid was done by comparing the planned route with the actual route driven. The comparisons were made by finding the distance normal to the route, input to ARVCOP, and the vehicle’s estimated positions, generated using a GPS-relative positioning technique; no vehicle heading information was used and only horizontal components were compared. These differences between planned and executed routes are the CTEs. As mentioned earlier, both the C-MIGITS III and the TACNAV were used as input to ARVCOP for these tests. Figure 10 shows an example of the raw data, with the ARVCOP planned route (blue) overlaid with the GPS estimated positions (red). In this example, ARVCOP used C-MIGITS III heading input updated at a 10-Hz rate.

Figure 10 illustrates how the AAV stayed on the planned course, showing only small deviations. The blue line represents the planned route and the red points are the GPS-estimated positions.

Figure 10. AAV planned and actual route, Run 2.

When TACNAV was employed to supply heading information, similar results were seen. Figure 11 shows the first run made with TACNAV heading estimates. The AAV stayed on planned route except for some minor deviations.

Figure 11. AAV planned and actual route, Run 5.

Figure 12 is of the second run using TACNAV heading information. In this instance, larger and more frequent excursions from the planned route are shown. The differences between Figures 11 and 12 are the result of the driver’s interpretation of the ARVCOP display. When the TACNAV was used as input to ARVCOP, the driver’s display showed greater instability than when the C-MIGITS III was used. The instability was a 1-Hz, few-degree shift in augmentation on the video corresponding to the TACNAV input rate. Figure 12 shows the result of the driver trying to follow all the augmentation shifts. When the driver ignored the sudden shifts in augmentation and drove a perceived average route, the resulting track was smoother, as Figure 11 shows. The 1-Hz input rate and the inherent TACNAV variations both contributed to the augmentation’s jumpy appearance.

Figure 12. AAV planned and actual route, Run 6.

Figure 13 shows the tracks of all the runs from the February 2009 tests that used the C-MIGITS III, except for runs 7 and 8. Run 7 was excluded because high surf caused its early termination when the vehicle was ordered to shore by the safety officer. The driver’s display was lost during Run 8 because of a loose cable and the test was aborted.

Figure 13. AAV planned and actual route, C-MIGITS-III heading data.

Table 1 (left) shows the CTE statistics for the C-MIGITS–III runs. Table 2 (right) shows the CTE statistics for the TACNAV runs.

Table 1 shows the CTE statistics for the C-MIGITS–III runs. Table 2 shows the CTE statistics for the TACNAV runs. Average speed over the course varied from 4 to 5 knots. It took about 15 minutes to drive the entire route.

Discussion

Comparison of the heading estimates between the C-MIGITS III and the TACNAV estimates showed variations of about 3 to 5 degrees, after removal of a bias. Investigation of the relationship of the heading angle error with the heading angle showed that after TACNAV calibration, significant heading error correlations remained in its estimates. Using the TACNAV as a source of heading information showed that the slower 1-Hz update rate and inherent variations of the sensor degraded the augmentation software’s performance. For example, when using the TACNAV, the augmented lane boundaries occasionally jumped a few degrees corresponding to the receipt of heading estimate updates. This was particularly evident after vehicle turns. The C-MIGITS III 10-Hz update rate and higher accuracy estimates enabled ARVCOP augmentation without distracting artifacts and provided the driver with more accurate navigation information. The ARVCOP-augmented objects were drawn on the video with a heading accuracy of about 6 degrees.

During February 2009 R&D tests, the AAV made eight surf runs using ARVCOP with C-MIGITS III input and two runs using TACNAV input. CTE statistics for the ARVCOP C-MIGITS-III testing showed rms differences of about 2.9 meters. The ARVCOP TACNAV testing showed larger rms differences of about 4.9 meters. These statistics represent the rms error between the AAV’s planned and executed route.

Summary

AR technology provides a human-machine interface for a navigation system enabling precise maneuvering. ARVCOP presents navigation data so intuitively that operators are able to multitask as required in mission performance while still being able to precisely maneuver. ARVCOP proved the concept of AR-based precise navigation in rigorous operational scenarios with the U.S. Marine Corps (USMC).

Test results for the R&D commercially available civilian GPS/INS hardware provided CTE of mean 2.1 meters and standard deviation of 2.0 meters. Operational hardware was evaluated in July 2009 over four days of testing, including 47 runs, in conditions with sea states ranging between 1 and 2.5, and many drivers. In 2010, at NSWCDD and Naval Surface Warfare Center, Panama City Division (NSWCPC), land demonstrations were performed with similar hardware navigating cleared paths through simulated mine fields at night. Vehicles were able to transit cleared routes with no external markings. The Naval Sea Systems Command Program Manager, (PMS 495), Mine Warfare Office, is now installing ARVCOP on USMC AAVs.

Acknowledgments

This work was sponsored by Brian Almquist, program officer, Ocean Battlespace Sensing Science and Technology Department, Office of Naval Research. LtCol Brian Seiffert, USMC, acting director of the Amphibious Vehicle Test Branch (AVTB), Camp Pendleton, supported the demonstration. GySgt Chapa and SSgt Schaefer, USMC, coordinated the AVTB effort. Kennard Watson, NSWCPC, coordinated the Camp Pendleton test plan. William Chambers, Maritime Technology Consulting LLC, Udayan Bhapkar, Andrew Sutter, and Alan Evans, NSWCDD, supported the tests and evaluations. Ronald Paradis, KVH Industries, Inc., supported heading sensor calibration.

Manufacturers

The C-MIGITS III is made by Systron Donner Inertial Division (www.systron.com) and TACNAV by KVH Industries (www.kvh.com).

Tom Zysk (captain, U.S. Navy, retired) has more than 35 years of experience in the Department of Defense and industry. He held positions with Raytheon and General Dynamics before joining Technology Systems Inc.

Jeffory Luce is a senior program manager at Technology Systems, Inc. (TSI). As lead for the ARVCOP program, he successfully transitioned TSI’s first project to a Program of Record.

James Cunningham has worked in GPS research and development at the Naval Surface Warfare Center, Dahlgren Division, for more than 25 years

System integrators have come to rely on the centimeter-level positioning accuracy made possible with real-time kinematic (RTK) commercial GPS receivers. Many authorized defense customers rely on access to the Precise Positioning Service (PPS) for single-point positioning. The OEM625S will combine a commercial dual-frequency NovAtel GNSS receiver with an L-3 IEC XFACTOR SAASM in a single card solution, reducing overall size and power requirements for end customer applications.

The OEM625S will maintain NovAtel’s OEMV-2 form factor, ensuring a successful drop-in replacement and backward compatibility for existing customers. Integrators can continue to use their existing user interface, which will be enhanced with OEM625S logs and commands for SAASM functionality.

NovAtel’s well-established, comprehensive set of software commands facilitates system integration, NovAtel said. The SAASM position is provided via a dedicated communication port, as well as through NovAtel’s software command protocol, allowing for maximum flexibility.

“For the past 17 years NovAtel’s customers have enjoyed great success in integrating our OEM family of high-precision receivers into a wide array of defense applications,” stated Graham Purves, executive vice president of NovAtel. “Adding the L-3 XFACTOR SAASM to our receiver card will allow defense customers to continue to use our products in the most demanding military environments.”

Ric Pozo, general manager of L-3 IEC’s Navigation Systems business unit, commented, “We are pleased to collaborate with NovAtel and provide the warfighter this highly flexible and capable GPS SAASM product. Our combined teams are looking forward to bringing this one-of-a-kind solution to market.”

NovAtel will accept orders for the OEM625S from authorized customers starting in the third quarter of 2012.

Figure 1. Autonomous air refuleing operational view.

By Alison K. Brown, Dien Nguyen, and Paige Felker, NAVSYS Corporation, Glenn Colby and Frank Allen, PMA-268 NAVAIR

An alternative precision GPS architecture, Precision RELNAV, enables an airborne tanker plane and a Navy unmanned combat aircraft to navigate independently to a high degree of precision without requiring carrier-cycle ambiguity resolution using precision GPS ephemeris updates to a tightly coupled GPS/inertial solution onboard each aircraft. The solution rivals that of conventional relative kinematic techniques while providing more robust positioning that reduces message traffic between aircraft and does not require a long filtering time.

Naval Unmanned Combat Air System (N-UCAS) is the U.S. Navy’s program to demonstrate technologies and reduce risk for unmanned, carrier based strike and surveillance aircraft. The Unmanned Combat Air System Carrier Demonstration (UCAS-D) program is specifically maturing technologies for unmanned carrier operations and Autonomous Aerial Refueling (AAR). Successful demonstration of UCAS-D technologies provides for transition and risk reduction to future unmanned and manned programs.

A key enabler for N-UCAS is the ability to perform AAR so that the N-UCAS can support long duration missions. As shown in Figure 1, the intent is for AAR operations to mirror current manned Aerial Refueling operations as much as possible and to operate using existing Navy probe and drogue and US Air Force boom receptacle refueling methods.

The planned refueling architecture for probe and drogue and boom-receptacle refueling developed by PMA-268 is shown in Figure 2 and Figure 3. For both of these architectures, the GPS/inertial navigation system on the UAS and tanker are used to calculate a precise relative position to be used by the UAS to approach the tanker from astern. For drogue systems, the final connection to the basket is performed using aiding from a laser-based drogue positioning system. In addition, an optional machine vision system is used to aid both methods of refueling from the receiver. Under the UCAS-D demonstration program testing is being conducted with surrogate aircraft to verify the CONOPS procedures and performance of the precision GPS/inertial navigation solution alternatives being evaluated. NAVSYS is supporting this program through a Small Business Innovation Research (SBIR) contract and is demonstrating a Precision-RELNAV (P-RELNAV) tightly coupled GPS/inertial solution that improves the robustness of the relative navigation solution as described in the following sections.

Figure 2. Probe and drogue refueling architecture.

Figure 3. Boom receptacle refuleing architecture

Precision RELNAV Algorithm

The first method that PMA-268 implemented for computing a relative GPS solution used the GPS/inertial integration approach illustrated in Figure 4. The inertial navigation solution from both aircraft was used to calculate the relative inertial vector e that is used for the real-time AAR guidance. The tanker’s raw GPS observations are also passed over the data link to the UAS where a relative kinematic solution is calculated to derive the carrier-phase based relative position between the aircraft, a. This approach relies on solving for the integer carrier cycle ambiguities on the observations from the two aircraft using the same algorithms that were previously developed for use in performing GPS precision approach and landings on the carrier. The precise GPS relative position is then applied to calibrate the inertial derived relative position and the resulting GPS/inertial solution is used to calculate an offset to the center of the refueling envelope (u) for guidance of the UAS to connect to the receptacle.

Figure 4. Precision-GPS relative GPS positioning.

With the P-RELNAV approach shown in Figure 5, Precision GPS Ephemeris data is provided to both aircraft across the tactical data links using the NAMATH system. As shown in Figure 6, NAMATH provides global services across military tactical data links through the Joint Range Extension (JRE) to provide real-time corrections to the GPS system errors using Zero-Age Precision GPS Ephemeris data, which is refreshed by the GPS Control Segment every 15 minutes. The NAMATH system is currently being used operationally by the U.S. military to improve navigation accuracy and also precision weapons delivery.

Figure 5. Tightly-coupled P-RELNAV Solution.

Figure 6. NAMATH Precision Ephemeris Delivery.

Using the PGE corrections significantly reduces the errors on the GPS observations allowing the GPS/inertial solution to rapidly converge and not exhibit step changes during satellite transitions from the GPS system bias errors. The GPS/inertial Kalman Filter on the tanker is used to observe the residual errors from the GPS satellites being tracked, and these residuals (δf) are sent from the tanker to the UAS which applies these as an update to its internal GPS/inertial Kalman Filter. As shown below, this final correction sets both the tanker and the UAS on a precise common reference frame resulting in a high accuracy relative position being derived from the vector difference of the two tightly-coupled GPS/inertial solutions (e*).

Figure 7 shows the difference in the GPS position that is calculated using the Precision GPS Ephemeris as opposed to the Broadcast Ephemeris. This shows that over a month, there can be peak position excursions as high as 5 meters in the horizontal and 10 meters in the vertical based on the GPS broadcast ephemeris. With a GPS/inertial solution, these bias offsets will cause the solution to “trend” between different position bias offsets whenever the satellite selected set changes. This trending introduces significant errors into the relative inertial vector between two aircraft (e).

Figure 7. GPS Peak Position Errors from Broadcast Ephemeris Offsets (March 2010).

P-RELNAV Flight Test Set-Up

The P-RELNAV performance was tested using data collected on a UH-1 helicopter at Eglin AFB. Two independent GPS/inertial systems were mounted on the equipment plate below the aircraft (Figure 8) and a GPS reference receiver on the ground was used to calculate a kinematic position post-test using a Magellan ZXW receiver on the aircraft as a truth system. The PGE corrections were uplinked to the aircraft through EPLRS for use in calculating a PGE-corrected navigation solution. NAVSYS used recorded GPS and inertial data from a Kearfott KN4073 and a NovAtel/LN-200 inertial system provided by Dahlgren NSWC. The raw GPS (Pseudo-range and carrier phase) and IMU (high rate acceleration and angular rate) data was processed using our InterNav solution and also recorded for post-processing. This data was then played back through InterNav to calculate independent GPS/inertial tightly coupled solutions from the two inertial systems with and without the PGE corrections and to compare the performance of the absolute and relative solutions against the kinematic positioning truth data.

Figure 8. Flight test equipment.

P-RELNAV Flight Test Results

The P-RELNAV algorithms were implemented in our InterNav software package. This has been previously used to generate very high accuracy relative kinematic solutions for providing high-rate Time Space Position Information (TSPI) for instrumenting F-16 aircraft. The InterNav software was upgraded to apply the tightly-coupled GPS updates to the inertial solution using the PGE Zero-Age Differential GPS (ZDGPS) corrections, and also to apply the GPS residual updates (δf) in the UAS Kalman Filter to compute the P-RELNAV relative position solution.

Dual-frequency observations from the GPS receivers were used to correct for the ionospheric group delays in the solution.

The performance of the P-RELNAV solution was evaluated by comparing the results from the two independent inertial solutions for the same location on the UH-1 aircraft. Tests were conducted over multiple flights with the GPS antennas at different locations on the UH-1.

The results from the first flight test are shown in Figure 9 through Figure 13. Figure 9 shows the GPS/inertial results during the flight with a tightly-coupled solution but without PGE corrections. Figure 10 shows the GPS/inertial results during the flight with a tightly-coupled solution but with PGE enabled. Figure 11 shows the satellite visibility during the flight test. These plots show that the satellite geometry changes, dramatically affecting the inertial position covariance, whenever the satellites used in the solution change. The inertial filters these errors, but the relative solution is biased and drifts resulting in over 2 meter errors. In Figure 12 the same plot is shown when the PGE corrections are applied. This shows that the relative position error has been reduced to better than 1 m per axis and 35 cm 1-sigma. For flight critical operations, such as AAR, minimizing position excursions is essential. Figure 13 and Figure 14 show a statistical measure of the percentage of time that the data exceeds a horizontal or vertical threshold. This shows the benefit of the PGE corrections in removing GPS excursions caused by satellite ephemeris errors from the navigation solution. (See the Appendix for a definition of the Inverse Circular Error Probable (ICEP) metric and its comparison with other statistical measures).

Figure 9. Flight 1: Relative position of KN and NovAtel/LN200 GPS/INS solutions.

Figure 10. Flight 1: Relative position of KN and NovAtel/LN200 PGE enabled GPS/INS solutions.

Figure 11. Flight 1: Valid PRNs used in KN GPS/INS solution.

Figure 12. Flight 1: Relative Position of KN and NovAtel/LN200 PGE enabled GPS/INS solutions.

Figure 13. Flight 1: Horizontal ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions.

Figure 14. Flight 1: Vertical ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions.

Since both GPS receivers used in the test had a reasonably clear view of the sky, they were both tracking the same satellites. In the AAR CONOPS, the UAS approaches the tanker from below and so will have some satellites obscured from view by the tanker (see Figure 4). In this case, the use of different satellites can significantly increase the relative position error when PGE corrections are not available. In the case shown where one satellite was forced as a drop-out, the non PGE corrected vertical error grew to 4 meters for the relative solution.

Further improvements in the P-RELNAV performance will be achieved using the residual (δf) update mode in the InterNav Kalman Filter to set the estimated observation residuals for the common satellites to the same values for the UAS and Tanker GPS/inertial filters. This mode is currently being tested and the results will be presented in a follow-on paper.

Figure 15. Flight 1: Horizontal ICEP plot for PGE enabled GPS/INS and GPS/INS solutions. Different satellites tracked by the receivers.

Figure 16. Flight 1: Vertical ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions. Different satellites tracked by the receivers.

Conclusion

The P-RELNAV solution has the following advantages over using a conventional relative kinematic positioning solution in meeting the Automated Aerial Refueling precision positioning requirements.

• Fast initialization — does not require time for carrier ambiguity cycles to be resolved.
• Robust operation during satellite obscuration by the tanker — is not dependent on common satellites being maintained in view between platforms.
• Insensitive to loss of carrier lock — does not require cycle ambiguity reinitialization if carrier lock is lost during the UAS approach to the tanker.

Work is proceeding on testing the P-RELNAV solution. Additional test data is being collected for performance evaluation under the UCAS-D demonstration program using dual aircraft as surrogates to demonstrate the P-RELNAV performance and compare the benefits of the P-RELNAV tightly coupled approach with the PGPS kinematic solution.

This work was sponsored under NAVAIR contract N68335-10-C-0094. The authors gratefully acknowledge the support of PMA-268 and the assistance of NSWC Dahlgren in collecting the flight test data and providing the truth reference for the P-RELNAV analysis.

Appendix: Inverse Circular Error Probable (ICEP)

For safety-of-life applications, the statistic of the excursion events, for example when a horizontal error is outside the safe error bound, is often more important than the knowledge of the percentage of points that are within a smaller error bound, such as CEP or DRMS. These excursion, or low probability, statistics can be examined with the Inverse Circular Error Probability (ICEP) function. The ICEP provides the horizontal position error (HPE) with a specified probability that a result could be outside this value. An optional input to the function is a filtering time constant, with the filter applied to the time-series horizontal error data before calculating the ICEP. This separates the effect of bias errors from short term noise errors that could be filtered (for example with an inertial unit) from the HPE.

HPE = ICEP (P%, τ)

Where
HPE= Horizontal Position Error value [m]
P% = Percent of total horizontal errors (x) that are larger than HPE
τ = filter time constant to reduce short term white noise

Note that the Circular Error Probable (CEP) which is the radial value that encloses 50% of the positioning results is closely related to ICEP, with
CEP = ICEP(50%, 0)

Also the R95 which is the radial value that encloses 95% of the positioning results is related to ICEP, with
R95=ICEP(5%,0)

Other common statistics used are the DRMS and 2DRMS values which are defined below, are also related to ICEP through the following equations.

For a Gaussian, uncorrelated error distributions with sigma of one meter in the range and azimuth axes, the ICEP is shown in Figure A-1 in blue. For each horizontal position error value, the ICEP gives the percentage of the distribution that has larger errors. Also shown on this plot are the CEP, DRMS, 2DRMS and R95 values which match the 1-sigma scale factors shown in the table above. Figure A-2 is the same data with a log₁₀ plot. In this plot the y-axis is probability rather than percent. This plot is useful for examination of outlier behavior, as it shows low probability events more clearly.

Figure A-1. ICEP(P,0) for a Gaussian Distribution with 1 m 1-sigma.

Figure A-2. Log Scale ICEP(P,0) for a Gaussian Distribution with 1 m 1-sigma.

Alison Brown is president and chief executive officer of NAVSYS Corporation, which she founded in 1986. NAVSYS Corporation specializes in developing next generation Global Positioning System (GPS) technology. She has a Ph.D. in mechanics, aerospace, and nuclear engineering from UCLA.

Dien Nguyen works for NAVSYS Corporation as a research engineer specializing in Kalman filtering estimations, kinematic positioning, and related navigational optimization techniques. He holds an M.S. in electrical engineering from Clemson University.

Paige Felker is a research engineer in the Algorithms and Analysis group at NAVSYS Corporation. She holds an M.S. in aerospace engineering from the University of Texas at Austin.

Glenn Colby is the chief architect for the Navy Unmanned Combat Air System at the Naval Air Systems Command in Patuxent River, Maryland. He has led the research, development, and testing of advanced aircraft, navigation and communications systems for more than 26 years. He received his B.S. in aerospace engineering with honors at the University of Virginia in 1984.

Frank Allen is the technology manager for the Navy Unmanned Combat Air System at the Naval Air Systems Command. In the last 16 years he has worked in management of research and development of advanced aircraft navigation and communications systems. Frank received his M.S. in physics from Northeastern University.

A fuzzy tracking system performs as a narrow bandwidth tracking system in terms of noise reduction, and a wide bandwidth tracking system in terms of dynamic response, overcoming the contradiction between receiver bandwidth requirements using classical tracking techniques for either noise reduction or dynamic tracking.

Autonomous navigation systems onboard precision guided missiles or fighter planes depend on GNSS and its very weak signals for positioning and navigation. Performance of a GPS receiver usually depends on the phase-lock loops (PLLs) used to down-convert these weak signals and track their carrier phase and frequency. A PLL can properly work only if its bandwidth is wide enough to track the signal dynamics, which can be significantly high, given the extremely rapid movements, accelerations, and direction changes of a missile or plane. On the other hand, wide-loop bandwidths allow larger portions of noise and interference to enter the tracking loops and disturb the signal tracking process. Excessive noise and interference can lead to loss of lock.

Aiding from a frequency lock loop (FLL) allows reducing the PLL bandwidth. This cannot prevent, however, frequent loss of lock and can be strongly affected by interference. The tradeoff between bandwidth requirements motivates design of alternative tracking systems replacing conventional FLL-assisted-PLLs.

We used fuzzy systems to design and test an innovative FLL-assisted-PLL. The output of a fuzzy controller that replaced standard loop filters drives the numerically controlled oscillator (NCO). The proposed fuzzy frequency phase lock loop (FFPLL) uses both frequency and phase discriminator outputs to generate the required frequency changes to tune the NCO, which in turn generates the local carrier for signal down-conversion.

The main core of any fuzzy system is its fuzzy sets or membership functions (MFs) that map input/output parameters into defined linguistic variables describing the input/output states. Loop discriminator outputs mainly depend on the incoming signal carrier-to-noise power density ratio (C/N₀) and have a probability density function (PDF) that, under lock conditions, can be accurately approximated by a Gaussian distribution. Although the mean of this Gaussian distribution is zero under normal tracking conditions, it can be affected by sudden changes in the presence of dynamics that can cause cycle slips and other phase errors. The standard deviation of this distribution is also dependent on the signal quality and hence on the interference level. For these reasons, the discriminator output values have been clustered into several overlapped Gaussian MFs that can linguistically describe their state. The variance of the Gaussian MFs assigned to the phase and frequency discriminator outputs are adaptively tuned according to the incoming signal quality. So any change in the interference power level leads to variations in the Gaussian MF variance to ensure accurate linguistic description of the discriminator output signal. The fuzzy rules are selected to tune the NCO and ensure accurate and robust signal tracking.

We assess performance of the fuzzy tracking system in the presence of different power levels of interference. To generate GPS signals corrupted by radio frequency (RF) interference, we used a hardware GPS signal simulator combined with two external signal generators, and applied different interference levels combined with missile harsh dynamics to test the proposed system. Results show that the fuzzy tracking system significantly improves system robustness and accuracy such that it is able to track very high dynamics with reduced tracking jitter. The system shows resilience against strong interference up to a certain extent where increasing jamming levels are compensated by the online adaptation of the MF distribution on the basis of a small amount of data or C/N₀ information.

The system performs favorably against standard tracking loops that cannot sustain the same level of dynamics and interference. The adaptive FFPLL can sustain interference power levels up to J/S = 40 dB. Even when the algorithm loses lock, a fast, reliable reacquisition is obtained when the interference power is reduced.

Theoretical Basis

Most physical processes are nonlinear in nature. Linear approximations and models are employed because linear systems are simple, understandable, and can provide acceptable approx-imations of the actual processes. Unfortunately, most tracking problems are too complex, and their linear approximation does not provide sufficient insight on the system in all environmental conditions.

Standard tracking loop filters are obtained by solving an optimization problem where the noise characteristics and the order of the signal dynamics are known. Different loop orders are obtained for different orders of dynamics. Moreover, the optimization problem is usually solved by considering a linear approximation of the loop. These assumptions are strong, but the standard solution can fail to provide satisfactory performance when the loop is no longer working in its linearity region, or the noise characteristics are not completely known. In such conditions, an approach based on a linguistic description of the system variables may be preferable. In that sense, fuzzy control systems provide sufficient tools for designing a robust alternative to standard loop filter.

In previous cases where researchers tried to use fuzzy techniques for PLL design, they used fuzzy logic controllers (FLCs) in parallel with a classic PLL architecture. We take a different approach, designing a new fuzzy rule-based tracking system to replace the standard FLL-assisted-PLL. The new system uses the noisy phase and frequency discriminator outputs and directly produces a control signal that represents the frequency change required by the NCO to maintain phase lock.

New Signal-Tracking Approach

GPS L1 signals consist of carrier, spreading code, and navigation data. To successfully demodulate the navigation data from the received signal, an exact carrier wave replica must be generated, generally using PLLs and FLLs. Figure 1 shows the basic block diagram of a standard PLL. The two first multiplication stages are required to wipe off the input signal carrier and pseudorandom noise (PRN) code required for any CDMA communication system. A local replica of the PRN code is provided by the delay lock loop (DLL) and is used to remove the PRN sequence from the incoming signal. The carrier loop discriminator is used to estimate the phase error between local and incoming carrier. The discriminator output, which represents the phase error, is then filtered and used to tune the NCO, which adjusts the frequency of the local carrier wave. Thus, the local carrier wave tends to be a precise replica of the input signal carrier.

FIGURE 1. Basic PLL block diagram (courtesy of Kai Borre).

PLL design is a challenging task, particularly if the receiver is affected by high dynamics, or if the input signal power is low due to signal interference or degraded environments. It is therefore desirable to provide robust algorithms for the PLL design.

FLLs are more resilient against signal dynamics and produce accurate velocity measurements. PLLs however also provide signal-phase information, leading to a simplified data demod-ulation process as compared to FLLs. Several attempts to combine the benefits of both loops have been done in the past, leading to various FLL-assisted-PLL schemes where the joint use of the two loops becomes an effective way to accomodate high signal dynamics. The ability of a tracking loop to track signal dynamics is also determined by the loop order. For high dynamic
scenarios, a 3rd order PLL is usually used as it is only sensitive to acceleration jerks. Higher-order PLLs can produce system instability and greater noise level. Figure 2 shows the loop filter of a typical 2nd order FLL-assisted 3rd order PLL, where T is the update period of the loop. All the gains shown in the figure are design parameters and function of loop bandwidths, B_np and B_nf , as reported in Table 1.

Figure 2. Schematic of a loop filter of a 2nd order FLL-assisted 3rd order PLL (courtesy of Elliot Kaplan).

Table 1. FLL-assisted-PLL loop filter gains.

The response of a GPS receiver to different signal-to-noise levels depends mainly on the code and carrier (phase/ frequency) tracking loop bandwidths. However, there is a trade-off between noise resistance and response to dynamics. Narrow bandwidth track-ing loops are more resistant to noise, which makes them suitable for moderate jamming environments. Wide bandwidth tracking loops are more responsive to dynamics. Thus, tracking loop bandwidth requirements for GPS receivers are conflicting. One solution is to adapt the tracking loop bandwidth to the receiver measured carrier-power-to-noise density ratio (C/N₀) and receiver dynamics. However, this approach can hardly solve for both concerns at the same time; trade-off must be found.

Automatic control methods based on artificial intelligence approaches (for example, fuzzy systems, neural networks, and genetic algorithms) have emerged as an alternative model to analytic control theory. One of the greatest advantages of fuzzy controllers is the simple and intuitive design. On the other hand, this simplicity is perhaps the primary cause of their initial slow acceptance among the control community.

Figure 3 shows the structure of the system design, where the standard loop filter is replaced by the proposed FFPLL controller. The fuzzy controller is composed of three consecutive layers named as fuzzification, fuzzy associative memories (FAMs, or fuzzy rules or fuzzy associations), and defuzzification layers.

Figure 3. Schematic diagram of a fuzzy tracking loop design.

The fuzzification layer is composed of a number of fuzzy sets characterized by MFs determined by the designer. These MFs are responsible for converting the crisp input values into linguistic values. The defuzzification layer is related to the fuzzification layer through the FAM rules that compose the second layer. FAM rules operate in parallel and to different degrees. Each is a set-level implication and represents ambiguous expert knowledge or learned input-output transformations. The system nonlinearly transforms exact or fuzzy state inputs to a fuzzy set output. This output is defuzzified with a centroid operation to generate an exact numerical output.

System Design

The fuzzy frequency/phase tracking system is designed to rapidly recover the signal frequency in the presence of large frequency errors, that is, after acquisition/reacquisition, and to behave as a PLL, with precise phase recovery, in the case of small frequency errors. The fuzziness of the system inputs is mainly due to the low power of GPS signals with respect to thermal noise, the main source of phase/frequency jitter. Noise distribution then plays a major role in the system design. This is why an a priori knowledge of expected signal parameters such as C/N₀ is essential. This knowledge can be achieved during signal acquisition or in the first stages of signal tracking. For example; a signal with a C/N₀ equals to 39 dB-Hz, in static condition and in an interference-free environment, is characterized by a phase discriminator output with a distribution approximately Gaussian as shown in Figure 4. The standard deviation of this signal, when using a standard PLL, can be theoretically calculated as follows:

where  (rad) is the standard deviation the dot-product discriminator, which also suits well the arctangent discriminator used in this research, T (s) is the predetection integration time and c / n₀ carrier to noise power expressed as a ratio (Hz).

Figure 4 shows the time-domain representation for the phase-discriminator output during tracking the incoming signal received from PRN 5 using a 4 Hz 3rd-order PLL in 1-millisecond coherent integration time and its histogram with the Gaussian function approximation. The corresponding Gaussian probability density function (PDF) in this case covers the signal expected values in standard tracking conditions at certain C/N₀ levels, and it can be linguistically described as zero-state if compared to the ideal phase discriminator output. The mean and standard deviation, which are the two main parameters that govern the Gaussian distribution function, are directly related to the signal dynamics and signal quality respectively.

FIGURE 4(a). Time domain representation of a PLL phase discriminator output, (b) Histogram and Gaussian approximation, (c) An example of mapping between PDF and MF.

Receiver dynamics can cause phase tracking errors, and hence the distribution mean will be shifted from zero. On the other hand, the changes in signal quality will produce changes in the standard deviation as illustrated in Equation (1). An appropriate mapping between the signal PDF and fuzzy MFs can be made, and in this case, the probability of occurrence described by the PDF will be replaced by a degree of occurrence sensed by a number of overlapped Gaussian MFs as shown in Figure 4(c).

Using this approach, both phase and frequency-error inputs in addition to the NCO tuning-frequency output domains are clustered into several overlapping Gaussian fuzzy sets, each of them describing a certain linguistic definition of input or output value (big, medium, small, zero, and so on). The number of MFs adopted for the fuzzy controller is reported in Table 2.

Table 2. Distribution of fuzzy membership functions.

The number of fuzzy sets associated with each fuzzy variable is a design parameter selected according to the required tracking accuracy. How much these contiguous sets should overlap is also a design issue depending on the problem at hand; too much overlap blurs the distinction between the fuzzy set values, whereas too little overlap can produce excessive overshoot and undershoot.

The fuzzy rules that relate all the linguistic variables can be expressed as:

R_i : if x₁ is Aⁱ₁ and x₂ is Aⁱ₂,

then y is Bⁱ. i = 1, 2 . . . 81

where x₁, x₂, and y are linguistic variables, and Aⁱ₁, Aⁱ₂ and Bⁱ are linguistic labels (or fuzzy sets) characterized by an MF. A defuzzification process is used to determine a crisp value according to the fuzzy output from the inference mechanism. The fuzzy centroid method, which calculates the center of the area of the infer
ence mechanism output possibility distribution, is used as defuzzification strategy in the FFPLL. The output y is obtained as

  (2)

where n is the number of fuzzy output sets, y_i is the numerical value of the ith output membership function, and u(y_i) represents its membership value at the ith quantization level. Table 3 shows the fuzzy rule table providing the human knowledge base of the controller.

Table 3. Fuzzy rules. The terms are B: big, MB: medium big, M: medium, S: small, and Ze: zero.

Gaussian MFs ended by trapezoidal rules were chosen as shown in Figure 5, Figure 6, and Figure 7, respectively. The variance of each Gaussian function can be changed according to signal noise level as described earlier, and online adaptation can be performed as described in a later paragraph. The FAMs are designed to act like an FLL for fast frequency tracking recovery in case of large frequency error indicated by the frequency discriminator. That can be seen in Table 3 in all the rules except when the frequency error is in the zero region. In this case it starts to look for the phase error, which is indicated by the phase discriminator for accurate phase tracking, and to extract the required data message.

Figure 5. Phase membership functions.

Figure 6. Frequency membership functions.

Figure 7. NCO tuning frequency membership functions.

Interference Effects

As shown in Equation (1), higher C/N₀ values ensure a small noise standard deviation, hence accurate and stable tracking. Increasing signal interference level will decrease the C/N₀ level.

Interference signal power usually changes according to the receiver maneuver by approaching or moving away from a jammer, jammer motion, or to the jammer power changes. These changes affect the effective C/N₀ on the receiver side. The analogy between Gaussian noise distribution and fuzzy MFs as shown in Figure 4 still holds, but a continuous change of the MF parameters — particularly the standard deviation — is required to cope with the C/N₀ variations.

For online adaptation of the MFs, the noise standard deviation associated with the phase and frequency discriminator outputs must be continuously estimated. This can be done using past samples from the phase and frequency discriminators. Small analysis windows, used for collecting past phase and frequency discriminator samples, should be used to properly follow rapid changes due to the interfering signal. A tradeoff between sensitivity and accuracy must be taken into consideration. For this research, we found a small analysis window with a width of 1 second to be enough for good sensitivity at high dynamics. Figure 8 shows the modified FFPLL system with the standard deviation estimation. This information is used for the online adaptation of the Gaussian fuzzy MFs.

Figure 8. Modified FFPLL with estimation of phase and frequency discriminator output standard deviation for MF online adaptation.

Test and Simulation

The primary equipment used for testing the proposed algorithm is a hardware simulator. The hardware configuration is capable of producing GPS signals in the L1, L2 and L5 frequencies in addition to adjustable additive interference through two separate signal generators. Several custom scenarios representing typical missile motion in space have been designed and tested. The radio frequency (RF) signals are collected through a front end after passing through an external low noise amplifier (LNA) using sampling frequency of 10 MHz, and saved for post-processing.

To assess performance of the tracking algorithm under interference and dynamic effects, we designed two categories of simulation scenarios. The first category is designed to test interference effects where a static receiver with gradually increasing interference level has been used. Both the interference and high dynamic effects are examined in the second category, in which scenarios of a missile that maneuvers near an interference source are designed. Four different tracking schemes are used for GPS signal tracking. They include the usage of a standard PLL with narrow and wide bandwidths (4 Hz and 14 Hz, respectively), FLL-assisted-PLL using narrow bandwidths (3/4 Hz), and finally the new FFPLL. The performance of each algorithm is evaluated by assessing the continuity of tracking during high dynamics, that is, the ability of the receiver to maintain lock, and the noise standard deviation of the estimated Doppler.

Interference Effect on Accuracy

The first test category involves studying the interference effect on GPS signal tracking capability and accuracy, using a custom scenario of a static GPS receiver with gradually increasing interference level. A continuous wave (CW) interference signal centered at the L1 frequency is combined with the generated GPS L1 signal and collected by the front end for post processing. Figure 9 shows the increasing interference effect on the signal quality particularly the signal C/N₀. In this scenario, the jamming to signal (J/S) interference power is gradually increased every 10 seconds in steps of 10 dB each starting from 0 dB higher than the GPS L1 power.

Figure 9. PRN 23 C/N0 level changes due to increasing interference power.

After reaching an interference power of about 40 dB higher than the GPS power, none of the tracking algorithms was able to track the signal and hence 40 dB is considered the maximum jamming tracking threshold. Figure 10 shows the estimated Doppler standard deviation for PRN 23 using the four tracking schemes described earlier at different interference levels. It is clear that the FFPLL scheme is superior to the other three conventional tracking schemes in terms of Doppler tracking jitter and hence tracking accuracy. The changes in C/N₀ level due to the increasing interference level affect the discriminators output noise level as described in equation (1). These effects can be noticed clearly in Figure 10. On the contrary, these changes are almost absorbed by the adaptive FFPLL, and hence the C/N₀ changes have a minimum effect on the Doppler tracking accuracy.

Figure 10. Doppler standard deviation calculated for PRN 23 using four tracking configurations.

Interference and High Dynamics

The second test category assesses the system performance under CW interference and high dynamics. The scenario considered here comprises the effect of missile maneuver near an interference source. Due to this maneuver, the GPS signal C/N₀ is changed with the distance from the interference source. The missile velocity in this scenario is increased to reach 300 meters/second performing hard maneuvers with acceleration up to 8 g and jerks up to 50 g/second. The same scenario is repeated five times with different CW interference powers. Due to missile high dynamics narrow bandwidth PLL or FLL/PLL was not able to p
rovide continuous signal tracking and losing lock occurred, that is why only a 14 Hz bandwidth PLL and FFPLL are considered. Interference powers generated are 20, 30, 40, 45, 50 dB respectively above normal GPS signal power. Figure 11 shows the 3D plot of missile trajectory and its maneuver near the jammer, while Figure 12 shows the effect of this maneuver on the signal C/N₀ for PRN 3 when a 40 dB interference signal is applied. C/N₀ increases and decreases according to the separation from the interference source.

Figure 11. 3D plot of the missile maneuver near an interference source.

Figure 12. C/N0 evaluated as a function of time for PRN 3 during maneuver around an interference source.

Tracking results show the ability of continuous tracking under interference level up to 40 dB higher than the GPS signal for both PLL 14 Hz and FFPLL. Higher levels of interference lead to tracking loss. FFPLL is able to recover tracking mode and retrieve the signal phase when interference source is disabled due to missile maneuver away from the jamming source whereas the wideband PLL is not able to retrieve back the signal phase in these high dynamics conditions.

Figure 13 shows the effect of adding a 40-dB interference signal on PRN 3 estimated Doppler and Doppler standard deviation respectively, using PLL 14 Hz and FFPLL. Tracking continuity is achieved using both algorithms; the interference signal greatly affects PLL tracking accuracy whereas FFPLL tracking accuracy is much better in both interference and interference free conditions.

Figure 13. Estimated Doppler calculated for PRN 3 using PLL 14 Hz and FFPLL at J/S = 40 dB.

Conclusions

The fuzzy tracking system solves the contradiction between receiver bandwidth requirements using classical tracking techniques for either noise reduction or dynamics tracking. It shows better performance in both cases since it performs as a narrow bandwidth tracking system in terms of noise reduction, and a wide bandwidth tracking system in terms of dynamic response.

The fuzzy tracking algorithm FFPLL provided tracking robustness in very high dynamics and signal interference up to 40 dB higher than GPS L1 power. The noise level calculated from the estimated Doppler is small, equivalent to results obtained with a very narrow PLL bandwidth under normal conditions. During high dynamics, tracking continuity is achieved using FFPLL with dynamic performance comparable to a wideband PLL or FLL/PLL. Signal tracking recovery is achieved if the interference power causing signal tracking denial is reduced or turned off.

Manufacturers

Spirent GSS7700 simulator, National Instruments PXI 5661 front-end.

Ahmed M. Kamel is a Ph.D. candidate in the Position, Location and Navigation (PLAN) Group at the University of Calgary. He holds an M.Sc. in electrical engineering from Military Technical College (MTC), Cairo, Egypt.

Daniele Borio received a Ph.D. in electrical engineering from Politecnico di Torino, Italy, was a senior research associate in PLAN Group, and is a post-doctoral fellow at the Joint Research Centre of the European Commission.

John Nielsen is an associate professor at the University of Calgary.

Gérard Lachapelle is professor of geomatics engineering at U. of Calgary, Canada Research Chair in wireless location, and head of the PLAN Group.