Analytically-guided-sampling particle filter applied to range-only target tracking

Abstract

Particle filtering (PF) is a popular nonlinear estimation technique and has been widely used in a variety of applications such as target tracking. Within the PF framework, one critical design choice that greatly affects the filter's performance is the selection of the proposal distribution from which particles are drawn. In this paper, we advocate the proposal distribution to be a Gaussian-mixture-based approximation of the posterior probability density function (pdf) after taking into account the most recent measurement. The novelty of our approach is that each Gaussian in the mixture is determined analytically to match the modes of the underlying unknown posterior pdf. As a result, particles are sampled along the most probable regions of the state space, hence reducing the probability of particle depletion. We adapt this proposal distribution into a new PF, termed Analytically-Guided-Sampling (AGS)-PF, and apply it to the particular problem of range-only target tracking. Both Monte-Carlo simulation and real-world experimental results validate the superior performance of the proposed AGS-PF over other state-of-the-art PF algorithms.