Telit has introduced the LE940A9 smart module, an automotive-grade module designed to support LTE Advanced Category 9 (Cat 9) networks.
The series offers three multi-band, multi-mode variants — including voice-over-LTE (VoLTE) — and is optimized for automobile manufacturers to deploy next-generation connected-car technology in world markets.
The LE940A9 is the latest addition to Telit’s xE940 family of automotive-grade modules. According to Telit, it delivers 450 Mbps download and 50 Mbps upload speeds with extremely low latency and advanced security, enabling the next wave of automobile industry’s applications and services which also serve as a springboard for autonomous driving.
https://youtu.be/kXBlY_L3OjI
“Digital transformation is driving the evolution of the connected car with major improvements in driver safety, new revenue streams, and an immersive connected experience,” Telit said in a press release. “With government safety mandates around the globe, added advancements in the connected world, there is greater demand for more value-add services and feature-rich in-vehicle applications.
The xE940A9 40×40 mm LGA form factor nests with the 34x40mm Telit xE920 automotive module family, offering flexibility for the OEM or tier-one integrator.
“From commercial and consumer telematics services, to autonomous driving and driver assistance features, along with a host of other applications dependent on remote software updates, including infotainment; secure, wired broadband-like speed is now a requirement. The evolution to high-speed wireless connectivity is only possible if powered by LTE Advanced, with little to no lag time, for the applications to work.”
The LE940A9 powers the entire connected-car platform, supporting current needs while including advanced features that enable future integration of upcoming value-added, telematics and managed services.
The module can run in-vehicle applications inside a secure processing environment from the built-in application processor, storage and memory. Automotive application programs can run entirely and securely on the module itself, protected by advanced cyber-security capabilities.
“In addition to serving as a significant advancement for the connected car industry, the LE940A9 series is a powerful testament to Telit’s continued technology leadership enabling the future of the connected car worldwide,” said Yossi Moscovitz, CEO of Telit Automotive Solutions. “Not only does the LE940A9 enable unprecedented applications with the speed and low latency of Cat 9 of the multi-mode variants, it also simplifies integration and reduces costs that help accelerate the development of our OEM partners’ global roadmaps.”
Tersus GNSS Inc. is now offering the INS-T-306, a GNSS-aided inertial navigation system. The INS-T-306 is the advanced module that combines GPS L1/L2, GLONASS, BDS navigation and a high-performance strap-down system. It is capable of determining position, velocity and absolute orientation (heading, pitch and roll) for any device on which it is mounted.
The launch of the INS-T-306 aims at facilitating motionless and dynamic applications that need high accuracy, such as vessels, ships, helicopters, unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs).
The INS-T-306 utilizes an advanced GNSS receiver, barometer, magnetometers, micro-electro-mechanical (MEMS) accelerometers and gyroscopes to provide accurate position, velocity, heading, pitch and roll of the device under measure.
Besides GPS L1/L2, GLONASS and BDS, the unit supports differential GPS and real-time kinematic (RTK). It is able to integrate into lidar (Velodyne, Riegl and Faro brands). The on-board sensor fusion filter, navigation and guidance algorithms, and calibration software inside all make INS-T-306 a commercially exportable GNSS-aided inertial navigation system.
u-blox and SIM Technology Group Limited of Shanghai, China, have announced that u-blox will not be acquiring the SIMCom cellular module product line as previously planned.
Despite best efforts on the part of SIM Technology Group and u-blox, the companies could not close the deal as originally intended and were unable to find alternatives that worked for both while sustaining the intended benefits. Both parties have therefore decided to terminate the Asset Purchase Agreement and Technology Assignment Contract with all ancillary agreements.
“While we are disappointed that the deal has not come to fruition, u‑blox and SIM Technology Group Limited continue to have a good relationship and expect to find other ways of working together in the future,” said u-blox CEO Thomas Seiler.
“Our strategy for cellular products remains focused on growth,” Seiler said. “For some time now we have been working on adapting our product range to achieve a stronger geographical diversification mainly for the Asian markets, where we make 50 percent of our global revenue. The strong move to LTE based connectivity will naturally open new strategic windows. Our strong focus and investment in our own chipset development especially for IoT applications is a key part of our strategy. Our guidance indicates a continued strong growth.”
As a result of this situation, u-blox has revised its guidance figures for 2017 back to levels as provided on Jan.11, 2017, and foresees for FY 2017 continued growth in all regions expecting revenues of between CHF 410 and 425 million, with EBIT in the range of CHF 60 to 65 million.
Spirent Communications plc is offering a solution that enables the civil aviation industry to evaluate the growing threat of GNSS interference, jamming and spoofing.
The new GSS200D Interference Detector was developed as part of Spirent’s partnership with Nottingham Scientific Limited.
Spirent’s GSS200D interference detector.
As skies and airports become more congested, there is increasing pressure on airports to be safely accessible at all times — which cannot be achieved by relying solely on non-precision approaches with high minimums or on today’s expensive and rigid ground-based infrastructure such as ILS (Instrument Landing Systems).
Ground-Based Augmentation System (GBAS) and instrument approach procedures based on Satellite Based Augmentation Systems (SBAS), such as Localizer Performance with Vertical Guidance (LPV) and Required Navigation Performance (RNP), provide Air Traffic Management with flexible, cost-effective alternatives while providing equivalent operational performance.
For example, the European Geostationary Navigation Overlay Service (EGNOS) launched the LPV-200 service in Europe that enables aircraft approaches without the need for visual contact with the ground until a height of only 200ft. above the runway.
With this service, accessibility, sustainability, efficiency and safety of the landing are greatly improved, especially in bad weather conditions.
Spirent’s new GSS200D solution monitors the radio bands used by EGNOS, as well as other GNSS augmentation systems such as the Wide Area Augmentation System (WAAS) or the GPS Aided Geo Augmented Navigation system (GAGAN), to ensure awareness of interference that could compromise positioning information.
Since local interference near the runway in the GNSS bands could degrade position accuracy or lead to a total loss of the navigation service, it is critical to continuously monitor and understand the RF environment and level of interference around airports.
The GSS200D collects quantitative data on interference allowing assessment of the risks, so that robust mitigation plans can be created. The new Spirent solution has been trialed at a number of European airports, and has collected numerous interference signatures from both unintentional man-made interference and intentional jamming.
“As more airports begin to use GNSS-based instrument approach procedures, they need to know what could be affecting their GNSS signals,” said Martin Foulger, general manager of Spirent’s positioning business. “With this latest solution we can detect interference in the key radio bands, based on levels defined by the United Nations International Civil Aviation Organization and European Organisation for Civil Aviation Equipment. This enables the aviation industry to gain a much better understanding of the electronic environment, helping to avoid dangerous situations going forward.”
For more information on Spirent’s GNSS testing solutions, visit the website. To learn how to test receivers of GPS, Galileo and other GNSS, download Spirent’s latest eBook.
Swift Navigation has released its first major firmware upgrade for its flagship product, the Piksi Multi GNSS module.
The upgrade is available at no cost to Piksi Multi users and expands on dynamic real-time kinematic (RTK) application support, increasing functionality for users, expanding use-case applications and allowing users to better leverage existing infrastructure and facilitate post-processing.
Firmware version 1.1 updates include:
Increased Data Output Rates to Support Dynamic Use Cases
GNSS Measurements (Raw Data) – Up to 20 Hz
RTK Output Support
Low Latency Mode – Up to 20 Hz
Time-Matched/Heading Mode – Up to 5 Hz
IMU (Raw Data) – Up to 200 Hz
Moving Baseline RTK Support. The capability to do real-time, precise relative positioning between two receivers where both receivers can now be in motion.
RTK-Based Heading Support. The capability to do real-time RTK-based heading for direction finding — even when stationary — without the need for expensive navigational equipment such as gyrocompasses.
Improved 1 PPS Support. The Piksi Multi Pulse Per Second (PPS) feature has been upgraded to support more customization.
Standalone RINEX Conversion Utility Tool. The tool allows end-users using RTKLIB, such as those with UAV surveying applications, additional tools to support their post-process kinematic needs.
Improved Compatibility with Existing Infrastructure (RTCM 3.1 Input). This added support enables end-users to better leverage existing base station infrastructure to receive RTK corrections (observations, station coordinates, etc.) from already deployed Continuously Operating Reference Stations (CORS).
Rockwell Collins and QinetiQ have signed a global alliance agreement to collaborate on the development of next-generation, multi-constellation open-service and secure GNSS receivers.
The effort will support the mission needs of military, government and critical national infrastructure.
The family of receivers being developed will provide military, government and professional users the flexibility of selecting relevant GNSS capability to meet operational, geographical or budgetary needs and provide GNSS accuracy and timing.
This will improve safety, increase mission effectiveness and reduce operational costs for ground troops, vehicles and high-dynamics GNSS-guided weapons, Rockwell Collins said.
Rockwell Collins is major contractor for secure military GPS receivers and QinetiQ is an expert in the field of open-service solutions with access to critical satellite navigation system technologies that enable the development of multi-constellation solutions.
“This alliance agreement with QinetiQ is a great opportunity to bring together our strengths,” said Colin Mahoney, senior vice president of international and service solutions for Rockwell Collins. “Working together, our customers will experience unprecedented levels of availability, accuracy and assurance of positioning, navigation and timing for conducting their missions.”
“As we move into the era of multi-constellation satellite receivers, this market-leading agreement and the investments of both companies sends a clear message to our customers and shareholders that QinetiQ and Rockwell Collins are taking every step necessary to stay at the forefront of GNSS technical development and product delivery,” said Steve Wadey, CEO of QinetiQ. “The development will be centered in Europe, led from the U.K., supporting the global market.”
OVER THE YEARS, many philosophers tried to describe the phenomenon of inertia but it was Newton, in his Philosophiæ Naturalis Principia Mathematica, who unified the states of rest and movement in his First Law of Motion. One rendering of this law states: Every body continues in its state of rest, or of uniform motion in a straight line, unless it is compelled to change that state by forces impressed upon it. Newton didn’t actually use the word inertia in describing the phenomenon, but that is how we now refer to it.
In his other two laws of motion, Newton describes how a force (including that of gravity) can accelerate a body. And as we all know, acceleration is the rate of change of velocity, and velocity is the rate of change of position. So, if the acceleration vector of a body can be precisely measured, then a double integration of it can provide an estimate of the body’s position. That sounds quite straightforward, but the devil is in the details. Not only do we have to worry about the constants of integration (or the initial conditions of velocity and position), but also the direction of the acceleration vector and its orthogonal components. Nevertheless, the first attempts at mechanizing the equations of motion to produce what we call an inertial measurement unit or IMU were made before and during World War II to guide rockets.
Nowadays, IMUs typically consist of three orthogonal accelerometers and three orthogonal rate-gyroscopes to provide the position and orientation of the body to which it is attached. And ever since the first units were developed, scientists and engineers have worked to miniaturize them. We now have micro-electro-mechanical systems (or MEMS) versions of them so small that they can be housed in small packages with dimensions of a few centimeters or embedded in other devices.
One problem with IMUs, and with the less-costly MEMS IMUs in particular, is that they have biases that grow with time. One way to limit these biases is to periodically use another technique, such as GNSS, to ameliorate their effects. But what if GNSS is unavailable? Well, in this month’s column we take a look at an ingenious technique that makes use of how the human body works to develop an accurate pedestrian navigation system — one whose accuracy has been checked using drone imagery. As they might say in New York, “Hey, I’m walking (with accuracy) here!”
Satellite navigation systems have achieved great success in personal positioning applications.
Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem.
One such solution is a personal pedestrian dead-reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians.
Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment — problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength.
In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors.
PDR DESIGN AND POSITIONING METHOD
Our plan in designing a pedestrian positioning system was to use a high-rate IMU device strapped onto the pedestrian’s shoe together with an EKF-based framework. The main idea of this project was to use filtering algorithms to estimate the errors (biases) accumulated by the IMU sensors. The EKF is updated with velocity and angular rate measurements by zero-velocity updates (ZUPTs) and zero-angular-rate updates (ZARUs) separately detected when the pedestrian’s foot is on the ground. Then, the sensor biases are compensated with the estimated errors.
Therefore, the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and, as a result, even relatively low-cost sensors can provide useful navigation performance. The PDR framework, developed in a Matlab environment, consists of five algorithms:
Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes.
IMU mechanization algorithm to compute the navigation parameters (position, velocity and attitude).
Pace detection algorithm to determine when the foot is on the ground; that is, when the velocity and angular rates of the IMU are zero.
ZUPT and ZARU, which feed the EKF with the measured errors when pacing is detected.
EFK estimation of the errors, providing feedback to the IMU mechanization algorithm.
INITIAL ALIGNMENT OF IMU SENSOR
The initial alignment of an IMU sensor is accomplished in two steps: leveling and gyroscope compassing. Leveling refers to getting the roll and pitch using the acceleration, and gyroscope compassing refers to obtaining heading using the angular rate.
However, the bias and noise of gyroscopes are larger than the value of the Earth’s rotation rate for the micro-electro-mechanical system (MEMS) IMU, so the heading has a significant error. In our work, the initial alignment of the MEMS IMU is completed using the static data of accelerometers and magnetometers during the first few minutes, and a method for heading was developed using the magnetometers.
PACE-DETECTION PROCESS
When a person walks, the movement of a foot-mounted IMU can be divided into two phases. The first one is the swing phase, which means the IMU is on the move. The second one is the stance phase, which means the IMU is on the ground. The angular and linear velocity of the foot-mounted IMU must be very close to zero in the stance phase. Therefore, the angular and linear velocity of the IMU can be nulled and provided to the EKF. This is the main idea of the ZUPT and ZARU method.
There are a few algorithms in the literature for step detection based on acceleration and angular rate. In our work, we use a multi-condition algorithm to complete the pace detection by using the outputs of accelerometers and gyroscopes.
As the acceleration of gravity, the magnitude of the acceleration ( |αk| ) for epoch k must be between two thresholds. If
(1)
then, condition 1 is
(2)
with units of meters per second squared. The acceleration variance must also be above a given threshold. With
(3)
where is a mean acceleration value at time k, and s is the size of the averaging window (typically, s = 15 epochs), the variance is computed by:
. (4)
The second condition, based on the standard deviation of the acceleration, is computed by:
. (5)
The magnitude of the angular rate ( ) given by:
(6)
must be below a given threshold:
. (7)
The three logical conditions must be satisfied at the same time, which means logical ANDs are used to combine the conditions:
C = C1 & C2 & C3. (8)
The final logical result is obtained using a median filter with a neighboring window of 11 samples. A logical 1 denotes the stance phase, which means the instrumented-foot is on the ground.
EXPERIMENTAL RESULTS
The presented method for PDR navigation was tested in both indoor and outdoor environments. For the outdoor experiment (the indoor test is not reported here), three separate tests of normal, fast and slow walking speeds with the IMU attached to a person’s foot (see FIGURE 1) were conducted on the roof of the Institute of Space Science and Technology building at Nanchang University (see FIGURE 2). The IMU was configured to output data at a sampling rate of 100 Hz for each test.
For experimental purposes, the user interface was prepared in a Matlab environment. After collection, the data was processed according to our developed indoor pedestrian dead-reckoning system. The processing steps were as follows: Setting the sampling rate to 100 Hz; setting initial alignment time to 120 seconds; downloading the IMU data and importing the collected data at the same time; selecting the error compensation mode (ZARU + ZUPT as the measured value of the EKF); downloading the actual path with a real measured trajectory with which to compare the results (in the indoor-environment case).
For comparison of the IMU results in an outdoor environment, a professional drone was used (see FIGURE 3) to take a vertical image of the test area (see FIGURE 4). Precise raster rectification of the image was carried out using Softline’s C-GEO v.8 geodetic software. This operation is usually done by loading a raster-image file and entering a minimum of two control points (for a Helmert transformation) or a minimum of three control points (for an affine transformation) on the raster image for which object space coordinates are known. These points are entered into a table. After specifying a point number, appropriate coordinates are fetched from the working set. Next, the points in the raster image corresponding to the entered control points are indicated with a mouse.
FIGURE 3. Professional drone. (Photo: DJI)
For our test, we measured four ground points using a GNSS receiver (marked in black in Figure 4), to be easily recognized on the raster image (when zoomed in). A pre-existing base station on the roof was also used. To compute precise static GPS/GLONASS/BeiDou positions of the four ground points, we used post-processing software. During the GNSS measurements, 16 satellites were visible. After post-processing of the GNSS data, the estimated horizontal standard deviation for all points did not exceed 0.01 meters. The results were transformed to the UTM (zone 50) grid system. For raster rectification, we used the four measured terrain points as control points. After the Helmert transformation process, the final coordinate fitting error was close to 0.02 meters.
For comparing the results of the three different walking-speed experiments, IMU stepping points (floor lamps) were chosen as predetermined route points with known UTM coordinates, which were obtained after raster image rectification in the geodetic software (marked in red in Figure 4).
After synchronization of the IMU (with ZUPT and ZARU) and precise image rectification, positions were determined and are plotted in Figure 4. The trajectory reference distance was 15.1 meters.
PDR positioning results of the slow-walking test with ZARU and ZUPT corrections were compared to the rectified raster-image coordinates. The coordinate differences are presented in FIGURE 5 and TABLE 1.
FIGURE 5. Differences in the coordinates between the IMU slow-walking positioning results and the rectified raster-image results. (Chart: Authors)
Table 1. Summary of coordinate differences between the IMU slow-walking positioning results and the rectified raster-image results. (Data: Authors)
The last two parts of the experiment were carried out to test normal and fast walking speeds. The comparisons of the IMU positioning results to the “true” positions extracted from the calibrated raster image are presented in FIGURES 6 and 7 and TABLES 2 and 3.
FIGURE 6. Differences in the coordinates between the IMU normal-walking positioning results and the rectified raster-image results. (Chart: Authors)FIGURE 7. Differences in the coordinates between the IMU fast-walking positioning results and the rectified raster-image results. (Chart: Authors)Table 2. Summary of coordinate differences between the IMU normal-walking positioning results and the rectified raster-image results. (Data: Authors)Table 3. Summary of coordinate differences between the IMU fast-walking positioning results and the rectified raster-image results. (Data: Authors)
From the presented results, we can observe that the processed data of the 100-Hz IMU device provides a decimeter-level of accuracy for all cases. The best results were achieved with a normal walking speed, where the positioning error did not exceed 0.16 meters (standard deviation). It appears that the sampling rate of 100 Hz makes the system more responsive to the authenticity of the gait.
However, we are aware that the test trajectory was short, and that, due to the inherent drift errors of accelerometers and gyroscopes, the velocity and positions obtained by these sensors may be reliable only for a short period of time. To solve this problem, we are considering additional IMU position updating methods, especially for indoor environments.
CONCLUSIONS
We have presented results of our inertial-based pedestrian navigation system (or PDR) using an IMU sensor strapped onto a person’s foot. An EKF was applied and updated with velocity and angular rate measurements from ZUPT and ZARU solutions.
After comparing the ZUPT and ZARU combined final results to the coordinates obtained after raster-image rectification using a four-control-point Helmert transformation, the PDR positioning results showed that the accuracy error of normal walking did not exceed 0.16 meters (at the one-standard-deviation level). In the case of fast and slow walking, the errors did not exceed 0.20 meters and 0.32 meters (both at the one-standard-deviation level), respectively (see Table 4 for combined results).
Table 4. Summary of coordinate differences between the IMU slow-, normal- and fast-walking positioning results and the rectified raster-image results. (Data: Authors)
The three sets of experimental results showed that the proposed ZUPT and ZARU combination is suitable for pace detection; this approach helps to calculate precise position and distance traveled, and estimate accumulated sensor error.
It is evident that the inherent drift errors of accelerometers and gyroscopes, and the velocity and position obtained by these sensors, may only be reliable for a short period of time. To solve this problem, we are considering additional IMU position-updating methods, especially in indoor environments. Our work is now focused on obtaining absolute positioning updates with other methods, such as ZigBee, radio-frequency identification, Wi-Fi and image-based systems.
ACKNOWLEDGMENTS
The work reported in this article was supported by the National Key Technologies R&D Program and the National Natural Science Foundation of China. Thanks to NovAtel for providing the latest test version of its post-processing software for the purposes of this experiment. Special thanks also to students from the Navigation Group of the Institute of Space Science and Technology at Nanchang University and to Yuhao Wang for his support of drone surveying.
MANUFACTURERS
The high-rate IMU used in our work was an Xsense MTi miniature MEMS-based Attitude Heading Reference System. We also used NovAtel’s Waypoint GrafNav v. 8.60 post-processing software and a DJI Phantom 3 drone.
MARCIN URADZIŃSKI received his Ph.D. from the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury (UWM), Olsztyn, Poland, with emphasis on satellite positioning and navigation. He is an assistant professor at UWM and presently is a visiting professor at Nanchang University, China. His interests include satellite positioning, multi-sensor integrated navigation and indoor radio navigation systems.
HANG GUO received his Ph.D. in geomatics and geodesy from Wuhan University, China, with emphasis on navigation. He is a professor of the Academy of Space Technology at Nanchang University. His interests include indoor positioning, multi-sensor integrated navigation systems and GNSS meteorology. As the corresponding author for this article, he may be reached at [email protected].
CLIFFORD MUGNIER received his B.A. in geography and mathematics from Northwestern State University, Natchitoches, Louisiana, in 1967. He is a fellow of the American Society for Photogrammetry and Remote Sensing and is past national director of the Photogrammetric Applications Division. He is the chief of geodesy in the Department of Civil and Environmental Engineering at Louisiana State University, Baton Rouge. His research is primarily on the geodesy of subsidence in Louisiana and the grids and datums of the world.
FURTHER READING
• Authors’ Work on Indoor Pedestrian Navigation
“Indoor Positioning Based on Foot-mounted IMU” by H. Guo, M. Uradziński, H. Yin and M. Yu in Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 3, Sept. 2015, pp. 629–634, doi: 10.1515/bpasts-2015-0074.
“Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning” by X. Zhang, H. Guo, H. Wu and M. Uradziński in Geodesy and Cartography, Vol. 63, No. 2, 2014, pp. 219–233, doi: 10.2478/geocart-2014-0016.
• IMU Pedestrian Navigation
“Pedestrian Tracking Using Inertial Sensors” by R. Feliz Alonso, E. Zalama Casanova and J.G. Gómez Garcia-Bermejo in Journal of Physical Agents, Vol. 3, No. 1, Jan. 2009, pp. 35–43, doi: 10.14198/JoPha.2009.3.1.05.
“Pedestrian Tracking with Shoe-Mounted Inertial Sensors” by E. Foxlin in IEEE Computer Graphics and Applications, Vol. 25, No. 6, Nov./Dec. 2005, pp. 38–46, doi: 10.1109/MCG.2005.140.
• Pedestrian Navigation with IMUs and Other Sensors
“Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors” by P.D. Duong, and Y.S. Suh in Sensors, Vol. 15, No. 7, 2015, pp. 15888–15902, doi: 10.3390/s150715888.
“Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker” by M. Placer and S. Kovačič in Sensors, Vol. 13, No. 8, 2013, pp. 9836–9859, doi: 10.3390/s130809836.
“Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning” by Z. He, V. Renaudin, M.G. Petovello and G. Lachapelle in Sensors, Vol. 13, No. 4, 2013, pp. 4303–4326, doi: 10.3390/s130404303.
“Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements” by A. Ramón Jiménez Ruiz, F. Seco Granja, J. Carlos Prieto Honorato and J. I. Guevara Rosas in IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, Jan. 2012, pp. 178–189, doi: 10.1109/TIM.2011.2159317.
• Pedestrian Navigation with Kalman Filter Framework
“Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU” by A.R. Jiménez, F. Seco, J.C. Prieto and J. Guevara in Proceedings of WPNC’10, the 7th Workshop on Positioning, Navigation and Communication held in Dresden, Germany, March 11–12, 2010, doi: 10.1109/WPNC.2010.5649300.
“A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors” by Z. Xu, J. Wei, B. Zhang and W. Yang in Sensors Vol. 15, No. 4, 2015, pp. 7708–7727, doi: 10.3390/s150407708.
Analog Devices and Renesas Electronics Corporation are collaborating on a system-level 77/79-GHz radar sensor demonstrator to improve advanced driver assistance systems (ADAS) applications and enable autonomous driving vehicles.
The new demonstrator combines the RH850/V1R-M micro-controller from the Renesas autonomy Platform and ADI’s Drive360 28nm CMOS RF-to-bits technology.
The system-level operation of these two technologies will enable earlier detection of smaller and faster moving objects at greater distances, according to the companies. It will also lower radar system integration efforts and reduce evaluation risks, development cost and time to market for automotive OEMs and Tier One suppliers, the companies said.
Analog Devices Drive360 28nm CMOS RADAR technology platform builds on the company’s established ADAS, MEMS, and radar portfolio to enhance sensor performance for ADAS applications with the world’s first automotive radar technology based on advanced 28nm CMOS with RF performance for target identification and classification. High output power enables greater range and identification of smaller objects, while lowest phase noise enables best unambiguous detection of smaller objects in the presence of large objects.
Renesas offer automotive end-to-end solutions from secure cloud connectivity and sensing to autonomous control. Renesas autonomy Platform is an open platform for ADAS and automated driving, supported by Renesas’ sustainable and scalable SoC and MCU roadmaps. The RH850/V1R-M MCU was specifically designed for use in radr applications.
The Analog Devices Drive360 28nm CMOS RADAR technology platform builds on the company’s established ADAS, MEMS and radar technology portfolio widely used throughout the automotive industry for the past 20 years.
ADI’s high-performance radar solution enables earlier detection of smaller and faster moving objects. High-output power enables greater range and identification of smaller objects, while low phase noise enables unambiguous detection of smaller objects in the presence of large objects. See Analog Devices’ Drive360 video here.
The new Renesas autonomy platform is an open, innovative and trusted platform for ADAS and automated driving, supported by Renesas’ sustainable and scalable SoC and MCU roadmaps, the company said.
The RH850/V1R-M MCU was specifically designed for use in RADAR applications as part of the sustainable and scalable portfolio. The new MCU includes optimized programmable digital signal processing, dual CPU cores each operating at 320 megahertz with high-speed flash of 2 MB and 2 MB internal RAM, while meeting industry temperature requirements.
Detection of artillery blasts at a near distance (0.15 miles or 0.24 km) using a single infrasound sensor, with the sensor amplitude trace over time shown on Infiltec’s Amaseis software data and visualization package, and using some basic bandpass filtering (5 to 25 Hz). The spikes are clearly visible as high amplitude impulses in the traces, confirming sensor detection.
Infrasound refers to sound frequencies below the threshold of human hearing, around 20 Hz or less. There are a variety of natural sources of infrasonic emissions, including thunderstorms, avalanches, meteors, earthquakes, volcanos, and windstorms as well as manmade sources of emissions, such as aircraft, heavy machinery, artillery, missile testing and road traffic. Infrasound is especially attractive from a sensing perspective due to its ability to propagate long distances while suffering little from atmospheric or environmental attenuation.
Blasts detected at 5.22 miles (or 8.4 km) are still detectable, but additional signal processing or wind-filtering techniques may make these impulsive signals more prominent above the noise.
In this work, we describe the development of a man-portable “tactical” infrasound field sensor array that is small, lightweight and can be rapidly set-up and torn-down as needed. The system is able to provide direction-finding capabilities to infrasound impulse sources with a directional accuracy of +/–3 degrees. Such information could be used for alternative positioning schemes, described in detail, or perhaps for direction-finding (homing) to acoustic sources of interest. Possible users could be military or search-and-rescue teams operating in GPS-denied environments; field researchers studying volcanology or seismology; or other geo-acoustic scientists and engineers.
Taoglas, a provider of Internet of Things (IoT) and GNSS antenna products, has released two new GPS certification testing services for Google and its device partners. The services are required for devices to meet Google’s new Street View auto-ready standard.
Auto-ready certification distinguishes 360-degree cameras that deliver accurately positioned 360 video, even at high speeds. Taoglas worked with Google to develop the performance requirements, as well as the test methodology used to establish a basic minimum level of GPS receiver performance.
The services are available at any of Taoglas’ design centers and labs in the United States, Ireland, Germany and Taiwan.
Compact wireless devices such as digital cameras with built-in GPS receiver systems contain complex electronic systems that can emit unwanted RF signals that can impact radio receiver performance. The effect of this RF noise can be combated with critical design decisions like the antenna, low noise amplifier, filters, and transmission line choice and implementation.
Taoglas’ new services will help device manufacturers objectively measure real-world performance to understand any GPS performance issues with their products. With this information, product manufacturers will know if their performance is optimized and will meet or exceed user expectation for the application at hand, as well as how it compares with their competitors.
“Google Street View provides people with a 360-degree view of the world, and to enable these services, we require highly accurate location data,” said Charles Armstrong, product manager at Google. “By working with Taoglas to establish a standardized compliance process, we’re helping device manufacturers understand our requirements for GPS performance and quickly deliver products that match and exceed those high performance standards.”
Taoglas is offering two levels of certification testing:
Street View Auto-Ready Conformance Testing (GSA.31) provides a quick verification of minimum performance (in a pass/fail manner) required to achieve Street View certification. Taoglas uses its GPS constellation simulator and anechoic chamber to verify that radiated tracking and acquisition sensitivity meet a minimum performance standard at 15-degree intervals in one hemisphere.
From these test results, manufacturers will be able to clearly see if the device’s GPS is performing adequately for basic location capabilities. The condensed period needed to run this test provides device manufacturers the best value to answer the question, “Is the GPS working optimally?”
A street view image of Guatemala. (Credit: Google)
Street View Auto-Ready Performance Testing (GSA.32) provides an absolute level of testing to assess the GPS receiver performance according to the optional Google Street View Assessment test procedures.
Taoglas uses its GPS constellation simulator and anechoic chamber to measure radiated tracking and acquisition sensitivity at 15-degree intervals in one hemisphere. These optional tests provide more insight into how well a device performs, providing absolute receive sensitivity performance data.
Testing results for both services include suggestions on next steps to resolve identified issues.
“This partnership with Google to deliver GPS testing solutions for Google Street View compliance is an excellent example of how we’re working successfully with the world’s biggest companies to delivering high-quality, reliable antenna solutions,” said Dermot O’Shea, co-CEO of Taoglas. “By certifying their products through Taoglas, device manufacturers will also be able to take advantage of Taoglas’ deep RF expertise, achieving success quickly and reducing time to market.”
“Street view” of the Ambrym Volcano, Vanuatu. (Credit: Google)
Precis-BX316R is a GNSS Post-Processing Kinematic (PPK) board for accurate positioning. It supports raw measurement output from two antennas: GPS L1/L2, GLONASS G1/G2 and BDS B1/B2 from primary antenna and GPS L1/L2 from the second.
The SD card on board (up to 32G) makes it convenient for users to collect data for post processing. Working with GNSS antennas, it can output stable measurement in challenging conditions, Tersus GNSS said.
Integrated with versatile interfaces and connectors, Precis-BX316R aims to facilitate applications such as precision navigation, precision agriculture, surveying and UAV, and enforcing effective GNSS data management.
Telit has introduced an ultra-slim family of smart antenna GNSS receiver modules. The fully integrated modules include a comprehensive feature set that eliminates the need for additional components. They are designed for Internet of Things (IoT) projects with size, cost and time constraints.
Easing the burden for developers with little or no RF design experience, the SL876Q5-A is compliant with regulatory and industry standards specifications. The module combines an omni-directional low profile embedded antenna and an internal RF switch. This combination is suitable for applications needing two antennas, such as personal trackers and alarms, which must be equipped with a main and a backup antenna when the signal becomes compromised.
A turnkey solution, the SL876Q5-A includes features such as an additional low noise amplifier (LNA), surface acoustic wave filter and efficient power management technology in an ultra-slim leadless chip carrier (LCC) package.
The inclusion of several low-power modes reduces total power consumption while maintaining position accuracy, which extends battery life — a critical requirement for wearables, personal trackers, and other battery-dependent applications.
“The SL876Q5-A delivers on everything our customers and partners have come to expect from the industry’s leading IoT solutions provider,” said Ronen Ben-Hamou, Telit’s EVP of products and solutions. “IoT developers continuously search for solutions that simplify the design process. We’ve developed a compact solution with features that deliver industry-leading performance without compromising performance. Not only does the SL876Q5-A eliminate the need for additional components in most use cases, but also cuts development time and costs considerably.”
SL876Q5-A Features
Full GNSS for exceptional coverage:
Quad-GNSS: GPS/ QZSS and GLONASS or BeiDou and it is Galileo ready
A-GNSS: Onboard generation and server-generated file injection that can be stored into the embedded flash memory
Omni-directional antenna design delivers high performance in sensitivity, tracking performance and accuracy
MEMS wakeup feature offers lowest power consumption
Built-in LNA for improved sensitivity
Primary port: UART, I2C, or SPI. Secondary port: UART or I2C, I2C supports MEMS wakeup only
Embedded RF switch allows easy integration with external antennas
Ultra-slim design, 11 x 11.9 x 2.3 mm LCC package for space constrained devices
Flash memory enables firmware upgrades, customization, and AGPS file storage, which is ideal for battery-dependent devices
Availability begins in the second quarter of 2017. Learn more about the new SL876Q5-A slim antenna module.
