PERFORMANCE ANALYSIS OF KALMAN-BASED FILTERS AND PARTICLE FILTERS FOR NON-LINEAR/NON-GAUSSIAN BAYESIAN TRACKING

Abstract In this paper, we present an overview performance analysis of Kalman-based filters and particle filters for Non-Linear/Non-Gaussian Bayesian tracking. The simulation results show that the particle filters have superior performance than the Kalman-based filters. Although the particle filters are time consuming, but in many situations such as the low data rate, low signal-to-noise ratio situations, the superior performance is very attractive.

[1]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  Nicholas G. Polson,et al.  A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling , 1992 .

[3]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[4]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[5]  Rudolph van der Merwe,et al.  Dual Estimation and the Unscented Transformation , 1999, NIPS.

[6]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[7]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[8]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[9]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[10]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[11]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[12]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[13]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..