A shrinkage probability hypothesis density filter for multitarget tracking

TL;DR

This paper introduces a novel shrinkage probability hypothesis density (PHD) filter within the finite set statistics framework for improved multitarget tracking in low SNR radar environments, enhancing accuracy over existing methods.

Contribution

It develops a shrinkage-PHD filter tailored for multitarget track-before-detect scenarios, optimizing the shrinkage parameter for better noise-target separation in low SNR conditions.

Findings

High-accuracy target tracking demonstrated in simulations.

Effective noise and target separation achieved with the shrinkage operation.

Outperforms traditional PHD filters in low SNR environments.

Abstract

In radar systems, tracking targets in low signal-to-noise ratio (SNR) environments is a very important task. There are some algorithms designed for multitarget tracking. Their performances, however, are not satisfactory in low SNR environments. Track-before-detect (TBD) algorithms have been developed as a class of improved methods for tracking in low SNR environments. However, multitarget TBD is still an open issue. In this paper, multitarget TBD measurements are modeled, and a highly efficient filter in the framework of finite set statistics (FISST) is designed. Then, the probability hypothesis density (PHD) filter is applied to multitarget TBD. Indeed, to solve the problem of the target and noise not being separated correctly when the SNR is low, a shrinkage-PHD filter is derived, and the optimal parameter for shrinkage operation is obtained by certain optimization procedures. Through simulation results, it is shown that our method can track targets with high accuracy by taking advantage of shrinkage operations.