Spectracom announced today a strategic partnership with Geodetics Incorporated of San Diego, California. The partnership includes a variety of initiatives to enhance each company’s customer reach, channels, products and technology.
The partnership includes a distribution agreement for Geodetics’ Geo-iNAV inertial navigation products. Spectracom will offer Geo-iNAV alongside its portfolio of precision timing, test and simulation equipment. Geo-iNAV is a fully integrated GPS-aided inertial navigation system that provides real-time, high-precision positioning and navigation solutions for manned and unmanned air, sea and ground vehicles. It combines GPS and sensor fusion to achieve centimeter-level real-time positioning and navigation for dynamic platforms.
“In line with our long heritage in delivering robust precision time and frequency products and services, we understand the challenges our customers face to adopt and adapt new and often disparate GPS and GNSS technologies,” said Spectracom President and CEO, Lisa Withers. “We believe our partnership with Geodetics will help to simplify the integration of complex positioning, navigation and timing technologies and provide our customers with a broad range of GPS and inertial navigation platforms readily suited for today’s dynamic and mobile environments.”
Geodetics President and CEO Lydia Bock added, “Spectracom’s global reach immediately widens the playing field for our inertial navigation products and technology. They have a keen sense of customer’s needs for the convergence of PNT in both military and commercial applications.”
As the GNSS eco-system expands to support mission critical applications, so must the prevalence of interoperability and signal fidelity, and ultimately PNT applications must be able to withstand the temporary loss of GPS due to factors such as signal obscuration, Spectracom said. As such, contemporary GNSS signal management solutions must be resilient to various GPS impairments as required of the application. Geo-iNAV delivers this capability through six configurations. It is available in commercial as well as SAASM GPS configurations as well as a choice of IMU depending on accuracy requirements. It offers a low SWaP (size, weight and power) profile for autonomous vehicles and payloads on manned vehicles to meet a wide range of applications.
As a part of Spectracom’s broader initiative to provide a comprehensive portfolio of GNSS signal management products, systems and services, the Geo-iNAV is the first in a series of compact and rugged solutions specific to PNT applications. In addition to simplifying complexity for its customers with contemporary, modular platforms, Spectracom’s market reach, together with the technical strengths of their partners such as Geodetics will accelerate time to market and aggregate the resources necessary to support unique and changing needs for precision references, simulation and signal test and analysis.
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.
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.
Trimble has introduced the AP20-C, the latest addition to its AP Series of embedded GNSS-Inertial OEM boards plus Inertial Measurement Unit (IMU). Using a compact, custom-built IMU based on commercial Micro Electromechanical Machined (MEMS) inertial sensors, the AP20-C enables system integrators to achieve high-rate position and orientation measurements with exceptional accuracy, Trimble said.
The announcement was made at AUVSI’s Unmanned Systems North America 2012 Conference and Exhibition being held this week in Las Vegas.
Featuring proven Applanix IN-Fusion GNSS-Inertial integration technology, the AP20-C is an embedded GNSS-Inertial OEM board set plus IMU designed for continuous mobile positioning in poor signal environments and high-accuracy direct georeferencing of imaging sensors. The AP20-C delivers full, high-rate position and orientation measurements at 200 Hz, ensuring it can be used in the most demanding mobile environments without sacrificing performance. It is fully compatible with the industry-leading Applanix POSPac MMS office software for enhanced accuracy using network differential GNSS.
“Compact in form and low in power consumption, the AP20-C can provide cost-effective, accurate, reliable and robust position and orientation measurements suitable for a broad range of survey and mapping applications, including airborne, terrestrial, and marine mapping as well as guidance for unmanned vehicle applications,” said Joe Hutton, director of Inertial Technology and Airborne Products at Applanix, a Trimble Company.
Advanced Navigation, a developer of 3D navigation technologies, has launched its Spatial product series, featuring NVS Technologies AG’s NV08C-MCM high-performance multiple GNSS-constellation receiver.
The Spatial is a ruggedized miniature GNSS/INS & AHRS system that provides accurate position, velocity, acceleration and orientation under demanding conditions. It combines temperature calibrated accelerometers, gyroscopes, magnetometers and a pressure sensor with an advanced GNSS receiver. These are coupled in a sophisticated fusion algorithm to deliver accurate and reliable navigation and orientation, Advanced Navigation said.
The Spatial product line takes advantage of the NV08C-MCM’s multi GNSS constellation support, ensuring high availability of navigation signals, high sensitivity, providing reliability, accuracy and performance.
Advanced Navigation is a privately owned Australian company that specializes in the development of 3D navigation technologies. The company’s engineers come from a background in mission critical robotics built to military specifications.
By Stéphane Gallot, Pascal Dutot, and Christophe Sajous
A new hardware assessment tool automates testing and mission replay, managing military GPS receiver input and output data, with an operational implementation and with a better control of initialization conditions, especially direct P(Y) acquisition. The test bench drives a GPS/Galileo simulator, a digital jammer, and software programs for visibility computation based on terrain modeling, and for multipath generation on 3D renderings.
Comprehensive assessment of military GPS receivers becomes more complex as they are integrated into advanced systems. To limit testing on systems under live conditions, laboratory evaluations with real elements are essential.
A new hybrid test bench called Statistical INERtial Gnss HYbrid in Simulation (SINERGHYS) is designed for governmental use to validate the integration of GPS/Galileo receivers within the navigation system for different platforms. As system-level requirements become more stringent, this bench has been designed to assess the behavior of the complete system in an operational context.
This new assessment hardware-in-the-loop tool is designed to automate testing and to replay missions with an operational implementation and with a better control of initialization conditions, especially direct P(Y) acquisition. This test bench drives many simulation tools: a GPS/Galileo simulator, a digital miniaturized jammer, and different softwares such as one enabling the computation of visibility depending on the terrain modeling, or one dedicated to the generation of multipaths on surfaces of realistic 3D scenes.
Figure 1. Depiction of SINERGHYS.Figure 2. Focus on the bench.
A Common Bench. Since 2000, with the arrival of the new cryptographic generation (the selective availability anti-spoofing module, or SAASM), the French government defence procurement agency (DGA) GPS laboratory decided to buy off-the-shelf GPS SAASM receivers that cover different form factors and applications. To test performance, it was necessary to acquire a test bench suitable for each GPS receiver. Testing procedures became more and more complex, and most of the manufacturer-provided benches could not perform every test required, such as direct P(Y) acquisition. To improve French expertise concerning GPS receivers, the DGA GPS laboratory decided to develop a common, generic test bench taking into account the integration constraints of each receiver. The perimeter of the hybrid test bench consists of a PC and a generic GPS test bench.
Figures 3 and 4 show examples of military GPS receivers integrated into the bench.
Figure 3. MPE-S (Ground-based application, Rockwell Collins).Figure 4. 1000S (Avionics,Thales).Figure 5. Embedded jammer.Figure 6. Jamming environment for a fighter aircraft. (Click to enlarge.)
Bench management is centralized, so test conditions are generic, and all simulation parameters are fully controlled. This enables users to display a unique view of the complete information and to be able to replay specific scenarios.
The bench manages military GPS receivers’ input and output data as described in the respective receivers’ interface control document (ICD) or interface specification: this enables, for example, the initialization of GPS receivers by sending precise time to facilitate direct P(Y) acquisition. This new bench is compatible with many GPS receivers with different form factors and applications.
Several receivers can be tested at the same time with the same software, so that the behavior of the GPS receivers can be compared in real time. Data from the different receivers can be observed on the same window of the graphic user interface (GUI). Specific data from ICDs can be displayed on the GUI. The user can visualize three different windows: the first is related to integrity, the second to alarms, and the third to cryptography. All the data output by the receivers can be recorded and replayed.
To facilitate and enhance trials on GPS receivers, the bench can use a Monte Carlo method, enabling sequentially and automatically chained scenarios, up to 10,000 test sequences, primarily for characterization of time-to-first-fix (TTFF).
Inertial navigation system (INS)/GPS hybridization in real time can be simulated via processing based on a Kalman filter of the information delivered by simulated INS and GPS. Loose and tight coupling can be selected through the GUI as well as filter parameters. The Kalman filter design is independent from the receiver and from the type of trajectory simulated. The user can decide whether the GPS receiver does receive aiding either from the simulated INS, or from the optimal navigation (output of Kalman filter).
Interfaces
The bench can interface with various external means and drive some tools and materials involved in the functioning of the bench.
With GPS Simulator. In the interface with the simulator, an intuitive GUI facilitates scenario preparation. When ready, SINERGHYS begins to drive the GPS simulator in remote-control mode. Any type of trajectory can be simulated with its operational environment modeled. The simulator outputs an RF signal to the receiver, and representative aiding, if required, by ethernet protocol to SINERGHYS.
With Jammer. Two types of interference signal generators can be used with the bench. Any available waveform can be generated. The bandwidth can go up to 20 Mhz for one generator and up to 80 Mhz for the other.
SINERGHYS is also compatible with a specific jammer called Embedded Jammer, designed to test vulnerability of GNSS systems (Figure 5).
The GPS receiver under test tracks the real GPS satellites combined with the simulated jamming signal. Thanks to the position and attitudes provided by the aircraft and to a modelized antenna diagram, the jammer computes in real time representative jamming that would be generated by real jammers.
This jammer works in two modes: localized mode (coordinates, jammer power, and waveform) and power profile mode. It was initially designed to be used inside an aircraft but can be used for laboratory testing as well.
The simulated environment is defined in the configuration software: waveform, emitter, scenario definitions (bands, number of emitters), and antenna diagram.
Four GNSS bands can be selected: GPS L1 and L2 (40 MHz) and Galileo E6 (40 MHz) and E5 (90 MHz). The embedded jammer can generate up to 14 simultaneous jammers per band, each with different waveforms. Therefore, up to 56 simultaneous jammers can be simulated.
The center frequency of the jamming signals can be chosen anywhere in the bandwidth. Modulation examples: continuous wave, broadband noise, binary phase shift keying), binary offset carrier (x,y), and so on.
Figure 7. Modulation examples.
External software interfaces fall under three categories.
Warfare. Electronic warfare software, which provides jamming coverage, performs a precise assessment of propagation (reflection and diffraction) of the interfering signals (depending on terrain modeling). Interference levels are transmitted to SINERGHYS during pre-processing.
Figure 8. Warfare GUI.
Satellite Tool Kit (STK). This software is designed to provide sophisticated modeling and visualization capabilities and performs functions critical to all mission types, including propagation of vehicles, and determination of visibility areas and times. STK generates paths for space and ground-based objects, such as satellites, ships, aircraft, and land vehicles. STK also provides animation capabilities and a two-dimensional map background for visualizing the path of these vehicles. Within SINERGHYS, STK is used for real-time visualization.
Figure 9. STK GUI.
Ergospace. This software is designed to generate multipaths, enabling the modeling of reflected paths of different satellite signals on surfaces of realistic 3D scenes. Pre-processed multipaths are sent to SINERGHYS and generated by the GPS simulator. The software is also used for real-time visualization.
Figure 10. Ergospace GUI.Figure 11. Example of the window showing the general state of the GPS receiver (c/n, svid, gram receiver and channel states, code and frequency tracked).
Operational Mission Characterization
The bench can evaluate and characterize receiver performance in most possible representative conditions.
Management of GPS Inputs/Outputs. Both black and red keys can be loaded inside the GPS receivers in both DS101 and DS102 protocols. This loading can be performed manually through key loaders such as KYK13 or DTD/ANCYZ10, but also through the host application with hexadecimal keys.
The bench can send commands to GPS receivers such as non-volatile memory erasure command, INS, precise time source, precise time and time interval (PTTI) activation commands, or choices between “mixed mode” and “all Y,” between “L1 primary” and “L2 primary,” and so on. Depending on user requirements, the bench can provide time, position, speed, almanac, ephemeris, or specific navigation sub-frames.
To test the jamming resistance of GPS receivers, it is essential to be able to provide INS aiding. SINERGHYS uses perfect or degraded aiding and adapts the format or the frequency for the considered GPS receiver.
Direct P(Y) acquisition functionality is an important case that needs to be evaluated. The GPS receiver needs a precise time to perform direct P(Y) acquisition. The time accuracy, from a few nanoseconds to several milliseconds, has a strong impact on the GPS behavior. A special delay box applied to the pulse-per-second signal of the GPS simulator in accordance with PTTI message (that is, time figure of merit), enables such a simulated accuracy.
A standard IS 153-like interface was developed to display GPS data on a convenient GUI in order to have a common software to visualize output data from the GPS receivers. The user can also visualize some specific data from GPS ICDs concerning integrity, alarms, and cryptography.
All receiver output data are recorded for later analysis.
Table 1. Example of Direct P(Y) acquisitions in accordance with time uncertainty (with times to get “GRAM state 5” and “protected status”).
Monte Carlo Trials
The bench enables sequentially and automatically chaining scenarios (up to 10 000 test sequences) to perform statistics on acquisition times. Indeed, it is primarily used for the characterization of TTFF. GPS signal acquisition is dependent on many different parameters, as described in Figure 12. To properly characterize receiver acquisition times requires a large number of tests. The comparison with GPS Receiver Applications Module requirements can be easily performed.
Figure 12. Setup parameters to study GPS signal acquisition.Figure 13. Example of a random selection for the position error.
One Monte Carlo trial consists of a repetition of unitary test: powering the receiver, then sending to the GPS receiver random errors of position, speed, time, levels of jamming, and finally stopping the test sequence on trigger. At the end of Monte Carlo trials, statistical computing enables accurate analysis and expertises.
The random selections are optimized to reduce the number of cases. The bench can replay a particular case: as the seeds are deterministic, a special case of Monte Carlo method can be selected and replayed.
Real-Time INS/GPS Data Fusion
The information delivered by INS and GPS are processed by a Kalman filter. The INS trajectory is provided by the simulator or by an external file.
Two types of coupling are considered: loose coupling with position and velocity information, and tight coupling with pseudoranges and delta ranges to estimate errors. In both cases, the GPS receiver receives aiding from either the simulated INS or the optimal navigation (Kalman filter output).
Figure 14. Example of an optimal navigation along a specified trajectory in a jamming environment.Figure 15. Position and velocity errors and navigation corridor.
The purpose of the Kalman filter is to estimate the navigation errors (position, velocity, and attitudes) and sensor errors (INS, GPS).
The filter design is original because it is independent from the receiver under test and from the type of application (hardiness privileged with reference to jamming). It is also able to estimate the time offset between position and velocity measurement on any GPS receiver under test.
Conclusion
SINERGHYS combines several resources into a single test bench. A complex mode can simulate an operational implementation with different interfaces and by chaining test sequences: receiver initialization, management of the switching of antenna patterns during a simulation, masking of GPS signals, management of jamming, INS/GPS data fusion, and so on. In this mode, missions can be replayed in a realistic environment. This bench is a complementary resource for flight trials and digital models because it can characterize the initialization phases with a good control of initial conditions. SINERGHYS enables users to know, as precisely as possible, the capabilities and limitations of a specific global navigation chain.
Manufacturers
SINERGHYS was developed by Bertin Technologies and specified by the French Ministry of Defense (MoD)DGA Information Superiority. It drives a Spirent GPS/Galileo simulator, Agilent 4431B and MXG generators, and software programs such as Analytical Graphics, Inc. (AGI) Satellite Tool Kit and Ergospace 3D scenes. The embedded jammer was developed by Ineo Defense in 2010 to MoD-DGA specifications.
Stéphane Gallot works at the French MoD (DGA Information Superiority) as a radionavigation expert. His particular interest is the integration of military GPS receivers including SAASM modules within French platforms.
Pascal Dutot is an architect engineer at the French MoD (DGA Information Superiority). His main activity is to optimize and control GPS integration in the global navigation chain.
Christophe Sajous works at the French MoD (DGA Information Superiority) as a radionavigation expert. He is also responsible for the “navigation per satellites” laboratory within the radionavigation department.
