Track‐before‐detect method based on cost‐reference particle filter in non‐linear dynamic systems with unknown statistics

Abstract

Detection of manoeuvring weak targets in radars often encounters circumstance where target movement is modelled by non-linear dynamic systems and received returns are corrupted by background noise of unknown statistics. It is known that the cost-reference particle filter (CRPF) is an efficient algorithm for state estimation of non-linear dynamic systems of unknown statistics. By combining an approximate logarithm likelihood ratio under the piecewise parametric model of signals with the CRPF algorithm, this study proposes a new track-before-detect detector, named CRPF-based detector, for manoeuvring weak target detection from received returns corrupted by background noise of unknown statistics. Experiments using simulated noise and real background noise of over-the-horizon radar are made to verify the CRPF-based detector. The results show that the CRPF-based detector has comparable performance with the two PF-based detectors for background noise of known statistics. For background noise of unknown statistics, the CRPF-based detector attains better detection performance than the two PF-based detectors where an assumptive probabilistic model is imposed on the background noise.