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Rao-Blackwellized Particle Filter for Asynchronously Dependent Noises

Yunqi Chen, Zhibin Yan*, and Xing Zhang
International Journal of Control, Automation, and Systems, vol. 19, no. 6, pp.2026-2037, 2021

Abstract : This paper develops Rao-Blackwellized particle filter with asynchronous dependence between system noise and measurement noise. It is pointed out that this dependence affects both the particle filter update step for the nonlinear sub-system and the Kalman filter update step for the conditionally linear sub-system in Rao-Blackwellized particle filter. A de-correlation method is suggested to deal with such influence. The optimal importance density function for sampling the nonlinear sub-state is found out, and a suboptimal one for approximating the optimal importance density function is proposed. The proposed methods are applied to target tracking to testify their effectiveness and superiority.

Keyword : Asynchronously dependent noises, de-correlation, importance density function, Rao-Blackwellized particle filter, target tracking.

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