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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2007 E90-A(6):1235-1239; doi:10.1093/ietfec/e90-a.6.1235
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Copyright © 2007 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Letters -- Systems and Control

Gauss-Newton Particle Filter

Hui CAO1, Noboru OHNISHI1, Yoshinori TAKEUCHI2, Tetsuya MATSUMOTO1 and Hiroaki KUDO1

1 The authors are with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464–8603 Japan. E-mail: souki{at}ohnishi.m.is.nagoya-u.ac.jp, 2 The author is with Information Security Promotion Agency, Nagoya University, Nagoya-shi, 464–8603 Japan.


  Abstract

The extened Kalman filter (EKF) and unscented Kalman filter (UKF) have been successively applied in particle filter framework to generate proposal distributions, and shown significantly improving performance of the generic particle filter that uses transition prior, i.e., the system state transition prior distribution, as the proposal distribution. In this paper we propose to use the Gauss-Newton EKF/UKF to replace EKF/UKF for generating proposal distribution in a particle filter. The Gauss-Newton EKF/UKF that uses iterated measurement update can approximate the optimal proposal distribution more closer than EKF/UKF, especially in the case of significant nonlinearity in the measurement function. As a result, the Gauss-Newton EKF/UKF is able to generate and propagate the proposal distribution for each particle much better than EKF/UKF, thus further improving the performance of state estimation. Simulation results for a nonlinear/non-Gaussian time-series demonstrate the superior estimation accuracy of our method compared with state-of-the-art filters.

Key Words: particle filter, better proposal distribution, extended Kalman filter, unscented Kalman filter, Gauss-Newton method

Manuscript received October 25, 2006. Manuscript revised January 22, 2007. Final manuscript received February 19, 2007.


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