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INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter

Published online by Cambridge University Press:  28 May 2010

Junchuan Zhou ,
Stefan Knedlik  and
Otmar Loffeld
Junchuan Zhou*
Affiliation:
(University of Siegen, Center for Sensorsystems (ZESS), Germany)
Stefan Knedlik
Affiliation:
(iMAR GmbH, St. Ingbert, Germany)
Otmar Loffeld
Affiliation:
(University of Siegen, Center for Sensorsystems (ZESS), Germany)
*
(Email: Zhou@zess.uni-siegen.de)
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Abstract

With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

Keywords

INS/GPS integrationAdaptive Kalman filteringUnscented particle filter
Type
Research Article
Information
The Journal of Navigation , Volume 63 , Issue 3 , July 2010 , pp. 491 - 511
Copyright
Copyright © The Royal Institute of Navigation 2010

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