Distributed Extended Kalman Filter for Position, Velocity, Time, Estimation in Satellite Navigation Receivers

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Date
2013-09
Authors
Jakubov, Ondrej
Kovar, Pavel
Kacmarik, Petr
Vejrazka, Frantisek
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Referee
Mark
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Společnost pro radioelektronické inženýrství
Abstract
Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.
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Citation
Radioengineering. 2013, vol. 22, č. 3, s. 776-790. issn 1210-2512
http://www.radioeng.cz/fulltexts/2013/13_03_0776_0790.pdf
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Peer-reviewed
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en
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Creative Commons Attribution 3.0 Unported License
http://creativecommons.org/licenses/by/3.0/
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