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Gauss-Newton Particle Filter Hui CAO Noboru OHNISHI Yoshinori TAKEUCHI Tetsuya MATSUMOTO Hiroaki KUDO Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E90-A No.6 pp.1235-1239 Publication Date: 2007/06/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e90-a.6.1235 Print ISSN: 0916-8508 Type of Manuscript: LETTER Category: Systems and Control Keyword: particle filter, better proposal distribution, extended Kalman filter, unscented Kalman filter, Gauss-Newton method, Full Text: PDF(88.9KB)>>
Summary: 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. | ||||||||||
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