Utilizing Out-of-Sequence Measurement for Ambiguous Update in Particle Filtering

Abstract

This paper proposes a novel method to cope with local measurement ambiguity problem in particle filtering. The ambiguity of the measurement has been attributed as a crucial cause of filter degradation and divergence. Given the observation that the ambiguous measurement update is contributed by not only the shape of the measurement model but also the prior distribution of the filter estimate, we adopt a solution to the out-of-sequence measurement problem on the framework of the particle filter with sequential importance resampling. Once an ambiguous measurement update is detected, the proposed method skips the measurement update at the time step and utilizes the measurement later when the particle distribution becomes adequate for the measurement update. This strategy provides a remedy to the ambiguity problem to obtain accurate current position estimate with lower covariance. Numerical simulation is presented to demonstrate effectiveness and performance of the proposed method. Compared to other methods, such as the standard particle filter, the auxiliary particle filter, the mixture particle filter, and the receding-horizon Kalman filter, the proposed method shows better performance in terms of root-mean-square error and estimated covariance.