Robust TMA using the possibility particle filter

TL;DR

This paper introduces the possibility particle filter as a robust alternative to traditional Bayesian filters for target motion analysis, demonstrating improved performance under model mismatch conditions.

Contribution

The paper presents the possibility particle filter within the sequential Monte Carlo framework as a novel, more robust method for recursive TMA, especially under model mismatch.

Findings

Superior performance against standard particle filter with model mismatch

Equal performance to standard particle filter with exact models

Effective in noisy, real-world scenarios

Abstract

The problem is target motion analysis (TMA), where the objective is to estimate the state of a moving target from noise corrupted bearings-only measurements. The focus is on recursive TMA, traditionally solved using the Bayesian filters (e.g. the extended or unscented Kalman filters, particle filters). The TMA is a difficult problem and may cause the algorithms to diverge, especially when the measurement noise model is imperfect or mismatched. As a robust alternative to the Bayesian filters for TMA, we propose the recently introduced possibility filter. This filter is implemented in the sequential Monte Carlo framework, and referred to as the possibility particle filter. The paper demonstrates its superior performance against the standard particle filter in the presence of a model mismatch, and equal performance in the case of the exact model match.