Adaptive sparse mixture particle filter

Abstract

We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the state space is directly reconstructed, which transforms the nonlinear estimation problem into a linear one through sparse representation. A number of significant grids are obtained based on the sparse vector, indicating the positions of multiple potential targets in the state space. Therefore, the multi-model posterior distribution of the state can be sparsely represented by a number of modes centering around the significant grids at each scan. Consequently, a novel algorithm named sparse mixture particle filter is proposed in this work, which provides a sparse representation of the multi-model posterior distribution by identifying the significant grids. Furthermore, a novel adaptive sparse mixture particle filter algorithm is proposed to tackle the high coherence and high computation burden problems, by constructing a compact dictionary based on the state space with low resolution. The simulation results show that the proposed adaptive sparse mixture particle filter based joint detection and tracking algorithm can successfully detect and track multiple targets, which appear and disappear at different times, as well as track closely spaced targets with similar dynamic model.