With modern global navigation satellite system (GNSS) signals, the FFT-based parallel code search acquisition must handle the frequent sign transitions due to the data or the secondary code. There is a straightforward solution to this problem, which consists in doubling the length of the FFTs, leading to a significant increase of the complexity. The authors already proposed a method to reduce the complexity without impairing the probability of detection. In particular, this led to a 50% memory reduction for an FPGA implementation. In this paper, the authors propose another approach, namely, the splitting of a large FFT into three or five smaller FFTs, providing better performances and higher flexibility. For an FPGA implementation, compared to the previously proposed approach, at the expense of a slight increase of the logic and multiplier resources, the splitting into three and five allows, respectively, a reduction of 40% and 64% of the memory, and of 25% and 37.5% of the processing time. Moreover, with the splitting into three FFTs, the algorithm is applicable for sampling frequencies up to 24.576 MHz for L5 band signals, against 21.846 MHz with the previously proposed algorithm. The algorithm is applied here to the GPS L5 and Galileo E5a, E5b, and E1 signals.

Jérôme Leclère

Cyril Botteron

René Jr. Landry

Pierre-André Farine

FFT Splitting for Improved FPGA-Based Acquisition of GNSS Signals

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

Heikki Hyyti

Arto Visala

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

Examining the performance of the GNSS PLL, this paper presents novel results describing the statistical properties of four popular phase estimators under both strong- and weak-signal conditions when subject to thermal noise, deterministic dynamics, and typical pedestrian motion. Design routines are developed which employ these results to enhance weak-signal performance of the PLL in terms of transient response, steady-state errors, and cycle-slips. By examining both single and data-pilot signals, it is shown that appropriate design and tuning of the PLL can significantly enhance tracking performance, in particular when used for pedestrian applications.

James T. Curran

Enhancing Weak-Signal Carrier Phase Tracking in GNSS Receivers

The knowledge of environmental maps (i.e., map-awareness)
can appreciably improve the accuracy of optimal
methods for position estimation in indoor scenarios. This improvement,
however, is achieved at the price of a significant complexity
increase with respect to the case of map-unawareness,
specially for large maps. This is mainly due to the fact that
optimal map-aware estimation algorithms require integrating
highly nonlinear functions or solving nonlinear and nonconvex
constrained optimization problems. In this paper, various
techniques for reducing the complexity of such estimators are
developed. In particular, two novel strategies for restricting the
search domain of map-aware position estimators are developed
and the exploitation of state-of-the-art numerical integration and
optimization methods is investigated; this leads to the development
of a new family of suboptimal map-aware localization
algorithms. Our numerical and experimental results evidence that
the accuracy of these algorithms is very close to that offered
by their optimal counterparts, despite their significantly lower
computational complexity.

Francesco Montorsi

Fabrizio Pancaldi

Giorgio M. Vitetta

Reduced-Complexity Algorithms for Indoor Map-Aware Localization Systems

Multipath propagation is one of the major sources of error in GPS measurements. In this research, a ray-tracing technique is proposed to study the frequency domain characteristics of multipath propagation. The Doppler frequency difference, also known as multipath phase rate and fading frequency, between direct (line-of-sight, LOS) and reflected (non-line-of-sight, NLOS) signals is studied as a function of satellite elevation and azimuth, as well as distance between the reflector and the static receiver. The accuracy of the method is verified with measured Doppler differences from real data collected in a downtown environment. The use of ray-tracing derived predicted Doppler differences in a receiver, as a means of alleviating the multipath induced errors in the measurement, is presented and discussed.

Naveen S. Gowdayyanadoddi

Ali Broumandan

Gérard Lachapelle

A Ray-Tracing Technique to Characterize GPS Multipath in the Frequency Domain

Automatic Dependent Surveillance-Broadcast (ADS-B) is an air traffic surveillance technology in which aircraft transmit position and identification. The development of space-based ADS-B will allow precise control of aircraft in areas that are not covered by radar, such as oceanic regions and high latitudes. The Royal Military College of Canada has developed a spaceborne ADS-B receiver scheduled to fly on the Canadian Advanced Nanospace eXperiment-7 (CanX-7) satellite. The payload is planned to collect data over the North Atlantic region, which will then be compared to truth data provided by air traffic services. A model was created to determine power levels arriving at the satellite to provide confidence in the ADS-B receiver and antenna proposed for CanX-7. The model takes into account neutral atmosphere and ionospheric effects, aircraft-satellite geometry, and antenna radiation patterns. A simulation was run by inserting real aircraft data from the North Atlantic Track System into the model and placing the satellite at altitudes of 400, 600, and 800 km. Results of the simulation indicate that power received at the satellite, ranging between −98.5 dBm and −103 dBm for the selected altitudes, will be sufficient to successfully conduct the mission.

Richard Van Der Pryt

Ron Vincent

A Simulation of the Reception of Automatic Dependent  Surveillance-Broadcast Signals in Low Earth Orbit

This paper presents an acoustic indoor localization system for commercial smart phones that emit high pitched acoustic signals beyond the audible range. The acoustic signals with an identifier code modulated on the signal are detected by self-built receivers which are placed at the ceiling or on walls in a room. The receivers are connected in a Wi-Fi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. The location of the smart phone is calculated by TDoA multilateration. The precise time measuring of sound enables high precision localization in indoor areas. Our approach enables applications that require high accuracy, such as finding products in a supermarket or guiding blind people through complicated buildings. We have evaluated our system in real-world experiments using different algorithms for calibration-free localization and different types of sound signals. The adaptive GOGO-CFAR threshold enables a detection of 48&#x25; of the chirp pulses even at a distance of 30&#x2009;m. In addition, we have compared the trajectory of a pedestrian carrying a smart phone to reference positions of an optic system. Consequently, the localization error is observed to be less than 30&#x2009;cm.

Alexander Ens

Fabian H&#xf6;flinger

Johannes Wendeberg

Joachim Hoppe

Rui Zhang

Amir Bannoura

Leonhard M. Reindl

Christian Schindelhauer

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

This paper demonstrates that automatic selection of the right interpolation/smoothing method in a GNSS-based network real-time kinematic (NRTK) interpolation segment can improve the accuracy of the rover position estimates and also the processing time in the NRTK processing center. The methods discussed and investigated are inverse distance weighting (IDW); bilinear and bicubic spline interpolation; kriging interpolation; thin-plate splines; and numerical approximation methods for spatial processes. The methods are implemented and tested using GNSS data from reference stations in the Norwegian network RTK service called CPOS. Data sets with an average baseline between reference stations of 60&#x2013;70&#x2009;km were selected. 12 prediction locations were used to analyze the performance of the interpolation methods by computing and comparing different measures of the goodness of fit such as the root mean square error (RMSE), mean square error, and mean absolute error, and also the computation time was compared. Results of the tests show that ordinary kriging with the Mat&#xE9;rn covariance function clearly provides the best results. The thin-plate spline provides the second best results of the methods selected and with the test data used.

Mohammed Ouassou

Anna B. O. Jensen

Jon G. O. Gjevestad

Oddgeir Kristiansen

Next Generation Network Real-Time Kinematic Interpolation Segment to Improve the User Accuracy

This paper deals with the design of a fault detection and isolation (FDI) system for an intelligent vehicle, a vehicle equipped with advanced driver assistance system (ADAS). The ADASs are outfitted with sensors for acquiring various information about the vehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed FDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies a generalized observer scheme (GOS). In the GOS, there is bank of extended Kalman filters (EKFs), each excited by all except one sensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults but one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The designed FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with diagnosing individual sensor faults and outputting fault-free vehicle states.

Wei Huang

Xiaoxin Su

Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

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International Journal of Navigation and Observation - Table of contents 2015

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International Journal of Navigation and Observation