Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection

TL;DR

This paper introduces a novel track-before-detect algorithm using a cost-reference particle filter bank, significantly improving weak target detection and tracking in low SNR environments compared to existing methods.

Contribution

The paper proposes a new two-layer hypothesis testing TBD algorithm based on CRPFB, enhancing detection accuracy and efficiency for weak targets.

Findings

Outperforms existing TBD algorithms in detection accuracy

Provides better tracking of weak targets in low SNR conditions

Demonstrates improved computational efficiency

Abstract

Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency.