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.

Initially, wireless carriers deployed 4G/LTE solely for data use. Without VoLTE9-1-1 capabilities, carriers must process emergency calls over 3G networks (circuit-switched fallback), even in areas where LTE is deployed. However, with TCS’ VoLTE9-1-1 service, they can now process 911 calls in an all-LTE environment, enabling them to reclaim or reuse 3G spectrum.

“As carriers increasingly move toward LTE networks, the ability to handle 911 emergency communications is critical,” said Thomas Ginter of TCS. “By leveraging VoLTE9-1-1, network operators are helping to ensure subscribers receive the responsiveness they need in an emergency situation, while expanding coverage to areas where 3G coverage is lacking.”

TCS VoLTE9-1-1 features:

• Call routing to the PSAP: The TCS VoLTE9-1-1 service routes a 4G/LTE-originated 911 call using coarse location via the route determination function component.
• PSAP telecommunicators can call back if disconnected: The TCS VoLTE9-1-1 service remains fully backwards compatible, supporting necessary functions such as providing PSAPs with full 10-digit subscriber callback numbers.
• Re-bid by a PSAP for precise location after call routing: The location retrieval function allows a wireless carrier complete flexibility in choosing its underlying high-accuracy location technology and supports updated/precise position requests.
• Emergency voice call continuity for location service: Location continuity and location delivery to the PSAPs are supported in usage scenarios where the 911 call switches from 4G/LTE to 3G/2G networks.
• Expansion beyond voice: As wireless networks advance, multimedia objects such as text, audio and video can be transferred to a compatible termination point with LTE IP networks, for example, an NG ESINet and i3 PSAP. Leveraging an all-IP network makes it easier and more cost effective to interconnect services.
• Small cell support: The TCS VoLTE9-1-1 solution supports small cells, including femtocells, microcells, and picocells, which are now commonly used in dense urban, indoor areas and enterprise networks.

TCS supports half of all U.S. wireless E911 calls, serving more than 140 million wireless and IP-enabled devices.  The company holds more than 280 patents, 43 of which relate to public safety, and more than 360 pending worldwide.

MAX-M5Q: ideal positioning solution for industrial M2M and Russia’s ERA-GLONASS emergency call system.

Swiss u-blox introduces the surface-mount MAX-M5Q, a compact satellite positioning module that supports GPS and GLONASS, as well as Japanese QZSS satellite GNSS systems. High-performance GPS/GLONASS parallel operation is also supported to enhance positioning speed and accuracy.

Designed for use in rugged environments and wide temperature range, MAX-M5Q is intended for industrial machine-to-machine (M2M) applications as well as Russia’s ERA-GLONASS emergency call system. MAX-M5Q enhances positioning in poor GNSS satellite visibility conditions as well as in high latitude and polar regions, u-blox said.

“With parallel GPS/GLONASS operation, MAX-M5Q is able to track all 50 and more U.S. and Russian satellites to deliver incomparable speed, accuracy, and positional availability,” said Thomas Nigg, vice president of Product Marketing at u-blox, “Its compact size and high-reliability makes it an ideal positioning solution for mobile resource management and ERA-GLONASS emergency call applications.”

With dimensions of 9.7 x 10.1 x 2.5 mm, MAX-M5Q is the newest member of u-blox’ MAX GNSS LCC module series. Additional features include autonomous A-GPS that reduces warm start TTFF by as much as 90%, and an embedded data logger which can store location information to internal Flash memory for up to 16 hours at 15 second intervals.

GPS+ delivers accurate location in real time directly to a smartphone, tablet or computer. eTrak products also contain geofencing capability, which triggers notifications if the device goes beyond preset boundaries. Additionally, the notifications provide a map and address with the location of the device.

“Small and large businesses will benefit from tracking their assets with loss prevention and cost savings,” said eTrak CEO, John Harris. “They will also be able to keep their employees safe and know if they are in danger or need assistance.”

Set-up and the use of eTrak products is simple, the company said. Users can go to eTrak.com and start tracking from any smartphone, tablet or computer and instantly see the location of the eTrak device. The eTrak consumer device is also equipped with an emergency panic button that sends help requests to predetermined recipients in the event of an emergency.

“eTrak is the size of a domino and weighs less than an ounce,” Harris said. “eTrak provides the longest battery life available, and is a low cost personal emergency-response product. And with Verizon’s industry-leading coverage map, it works almost anywhere.”

The eTrak product line encompasses commercial applications, as well as consumer segments including health-impaired, special needs, teens and pets. eTrak also supplies custom design and development services.

“We are excited and thankful for the opportunity to partner with LTI, a strong and respected leader in field laser data collection,” said Mark Milligan, president of F4 Tech. “The S-Series offers a cost effective solution that compliments the current product offerings.”

“Our product management team approached F4 Tech about a data collector solution, and they were quick to respond. The S-Series provides LTI with an ergonomic, rugged and reliable handheld unit for our Professional Measurement and Traffic Safety software solutions. We are excited to bring this solution to our customers,”said Derrick Reish, senior product manager of LTI.

Signal Sentry 1000, formerly known as GPS Interference, Detection and Geolocation, may now be deployed to collect actionable intelligence for law enforcement, such as tracking high-value targets and protecting critical infrastructure.

Signal Sentry 1000 is a proprietary product that leverages GNSS signal domain knowledge; it is based upon patented technology developed by Exelis through many years of designing and fielding electronic intelligence systems, ITT Exelis said.

“Exelis developed Signal Sentry 1000 to help protect critical infrastructure and to deliver intelligence to law enforcement operations that depend upon GPS availability,” said Kevin Farrell, positioning, navigation and timing general manager for Exelis Geospatial Systems. “Jamming devices can transmit signals capable of disrupting the synchronization of critical infrastructure, such as utility power grids, and timing information of financial transactions. This is why we are continually making improvements in our technology, and the latest milestone achievement is a testament to our goal to deliver actionable interference intelligence to agencies that rely upon GPS operational availability.”

Signal Sentry 1000 technology is a network of threat-detection sensors, which are part of a centralized server executing Exelis‐developed proprietary location algorithms. These sensors can be strategically located around areas of critical infrastructure, such as shipping ports, utilities and government facilities to automatically sense and locate any intentional or unintentional GPS jamming source. Should a threat be detected, users would receive accurate location information and actionable intelligence in order to determine an interference-mitigation plan.

“Signal Sentry 1000 builds upon Exelis expertise in the field of GPS and positioning, navigation and timing. Exelis payloads and payload components have been on board every GPS satellite for nearly 40 years,” said Farrell.  “Today, Exelis is involved in GPS modernization initiatives, building tomorrow’s GPS III satellite constellation by developing and integrating the navigation payloads. Exelis is also providing navigation processing components, precision monitor station receivers, and key components of the system security design for the GPS Operational Control System, also known as OCX.”

The app, called VRPETERS (Vehicle Rollover Prevention Education Training Emergency Reporting System), uses sensors and GPS capability built into smartphones that can detect rollovers. Once the app detects a rollover, it sends an automatic emergency e-mail and phone message with the coordinates of the accident location to family or emergency responders.

“The tractor is the main power source for field operations, and tractor rollover accidents have been killing people since the beginning of their use in agricultural production,” said Bulent Koc, assistant professor of agricultural systems management at MU and developer of the app. “More and more farmers are using their smartphones to monitor weather or calculate production inputs while operating machinery. Since they already have their phones with them, installing VRPETERS could help save lives.”

Data from the NIOSH show that one out of every 10 tractor operators will roll a tractor at least once. NIOSH also notes that only half of the 4.7 million tractors on U.S. farms have rollover protection. In order to minimize false alarm rollovers on the app, Koc and his research assistant Bo Liu designed a device that must be attached to the tractor. This device helps calculate the stability characteristics of the tractor and will provide a warning to the driver when the tractor approaches its rollover point.

“Many farmers think they can jump out of their tractors in the event of a rollover, but this isn’t the case usually,” Koc said. “Side rollovers can occur in just three-quarters of a second and most people need a second or more to react to an event. So, VRPETERS can benefit farmers when a rollover occurs because they often can’t reach their phones to make an emergency call.”

VRPETERS can benefit more than just farmers, as the app also can be used on construction vehicles, trucks, snowmobiles, military vehicles, riding lawnmowers and all-terrain vehicles.

In addition to the rollover device installed on tractors and other dangerous equipment, Koc and Liu designed another device that can be used with VRPETERS. This device can be installed on vehicles and can be used as a backup to stream data to a smartphone or tablet. “With this additional device, parents or fleet managers can obtain real time data on how machines are being used,” Koc said. “If the device detects improper operation, an intervention can occur before an accident happens.”

Initial testing of VRPETERS was done using a remote-controlled model tractor. Once fully tested on a standard tractor, Koc and Liu will look for an industry partner to market the app.

Indoor location research and fielded developments currently focus on consumer-level applications, mostly using mobile phone handsets, but this work will hopefully also benefit professional and high-precision uses of GNSS. Indoor location technologies could be of particular interest in machine control for warehousing, industrial assembly, indoor and even underground mapping, underground mining, in forestry where dense canopy virtually cuts out GNSS, construction, and other areas where sky-view is limited or negligible.

Tune in to Indoor Nav Webinar Thursday

Tune in to GPS World’s webinar, “Indoor Positioning and Navigation: Results of the FCC’s CSRIC Bay Area Trials,” on Thursday, April 18. Speakers include Khaled Dessouky (Technocom); Ganesh Pattabiraman (NextNav); Norm Shaw (Polaris Wireless); and Greg Turetzky (CSR). Registration is free.

Professional users will want to keep abreast of developments in the E-911 area, and be aware as achievable accuracies begin to approach what could be possible for precision applications. Right now, that’s maybe a pretty big stretch, but taking a look periodically is a good idea. A recent round of landmark tests by the Federal Communications Commission (FCC) provides just such an occasion for a look-in.

The U.S., E-911 legislation put in place back in 2001 required that both landlines and cellphones should provide the location of callers to within specific accuracy levels. Location information was to be sent transparently to Public Safety Answering Points (PSAPs) which would allow fire/rescue/police personnel to be dispatched to the location of the 911 call. For mobile phones, cellphone manufacturers and network providers forged ahead and implemented a number of location strategies using differing technologies — all require being outdoors where a clear sky-view is available.

GPS and augmented GPS technologies were only part of the cellphone solution. Other implementations included use of the cell-signal itself, along with an extensive database that can contain, amongst other things, signal attributes and network asset locations. Turns out that, today, around 60 percent of mobile phone calls are made within buildings, so the FCC started to investigate how to bring E-911 capability to indoor calls.

In 2011, the FCC commissioned a group called the Communications Security, Reliability and Interoperability Council (CSRIC), and Working Group 3 (WG-3) is the one currently investigating what can be done for indoor E-911 location. Drawn from interested industry participants, the WG-3 Location-Based Services (LBS) sub-group set about finding what technologies exist, how well they work, and how they could be applied to E-911. Now, there are a lot of people trying to crack this problem and many, many ways that it’s been tackled — all of which are at different stages of development and with differing levels of capability. In order to make definitive progress, WG-3 LBS decided that a test-bed was the best way to evaluate and compare what’s currently available.

Seven vendors signed up initially, but only three — NextNav, Polaris Wireless, and Qualcomm — completed the rigorous testing, which set out to basically establish horizontal and vertical accuracy, speed of location, and reliability and consistency of results for each system. The trial tested the performance of location systems across urban, suburban and rural areas in the San Francisco Bay Area. More than 13,000 test calls were placed from various tested technologies in 75 different indoor locations selected by participating public safety organizations from around the U.S. Click here for the full report.

In the tests, Polaris Wireless used an RF pattern matching/fingerprinting technique, Qualcomm used a hybrid Assisted-GPS (A-GPS)/Advanced Forward Link Trilateration (AFLT) system, and NextNav used wireless beacon technology. NextNav came out on top, and largely within the magical 50-meter “search ring” requirement, and was the only vendor to provide vertical location capability.

NextNav uses pressure transducers in its beacons and in the handheld units to accurately measure calibrated altitude — within about 2 meters — so it can actually report the floor where the handheld is located; it’s the only system tested that was able to do so. Apparently the use of MEMS pressure sensors in cellphones is forecast to increase to 681 million units in 2016, so this could be the right approach.

NextNav is focusing on the San Francisco market, where the company has fielded a significant number of beacons, but it has also placed beacons in another 40 metropolitan locations across the U.S. NextNav has acquired appropriate spectrum rights to transmit a 900-MHz “GPS-like” signal that’s synchronized to GPS. This enables good penetration into most urban buildings — both high-rise and those with fewer floors.

To support adoption of its solution, NextNav is working with a chipset manufacturer to incorporate processing of its location signal within an upcoming spin of an embedded cellphone chipset. While other solutions have adopted Wi-Fi and cell-signal solutions, NextNav contends that its approach is the most cost effective, as beacon deployment is geographically less dense and can be amortized over so many users.

NextNav Beacon.

Other solutions also apparently rely on the use of databases that store signal characteristics and a number of other parameters – the CSRIC report highlights the complexity this brings to database management and maintenance. NextNav also has a database, but this is basically to store records of location, cable configurations and calibration data. This is only used to ensure consistent performance of their system; it’s not required for network operation or location.

Higher precision applications would also benefit from this type of augmentation in the same way that WAAS users achieve higher accuracies, except this system uses local beacons, and there could be the potential for even higher precision with known fixed beacon locations within urban environments. As commercial UAV applications grow, it’s not impossible that there will be higher precision flight applications within cities, for geo-location surveying, building and outside appliance inspections, signal mapping, traffic mapping, road-work repair monitoring — in fact, many of the monitoring activities we see daily in towns and cities where a view of the sky can be particularly restricted.

The CSRIC participants are not the only ones pursuing the holy grail of indoor location. As mentioned, seven different location vendors/technologies began the process to demonstrate their performance indoors through the common test bed, but only three completed the process. The others remain highly motivated and involved, however, and at work tuning their varied solutions. The WG3 report states, “The following location vendors showed initial interest in having their technologies tested and highlighted through the test bed process, but ended up not participating in the Stage 1 test bed, for a variety of reasons.

• U-TDOA Positioning (TruePosition)
• DAS Proximity-based Positioning (CommScope)
• A-GNSS / Wi-Fi / MEMS Sensor Hybrid Positioning (CSR)
• LEO Iridium Satellite-based Positioning (Boeing BTL).”

Meanwhile, promising indoor location research goes on at a number of commercial and academic institutions, such as the University of Calgary PLAN group, which has focused on integration of Wi-Fi and GPS. An upcoming paper reports that Wi-Fi, using the 802.11 standards, can be employed in several different ways as a complementary positioning technology for GPS/GNSS navigation, and the two can be used in an integrated framework to provide a continuous and robust positioning service.

Another promising component for indoor location could be the recent release of a software application by Baseband Technologies, which can provide rapid ephemeris for up to 28 days, between ephemeris downloads from GPS directly or over cellphones from the Internet. But indoor location warrants much more extensive treatment than these few random comments — what’s summarized here are only some recent developments in E-911.

There will likely be another round of E-911 test-bed activities if funding and management issues are resolved. See CSRIC WG-3 LBS Subgroup member Greg Turetzky’s “Expert Advice” column from GPS World for perspective and a forward look. We can anticipate even wider participation by differing technologies and even greater levels of performance in future. Longer term progression towards higher precision professional applications seems to be inevitable.

Tony Murfin,
GNSS Aerospace