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

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.