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

• Published in: IET signal processing Vol. 8; no. 1; pp. 85 - 94
• Main Authors: Lu, Jin, Shui, Peng-Lang, Su, Hong-Tao
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.02.2014; Institution of Engineering and Technology; The Institution of Engineering & Technology
• Subjects: Algorithms; Applied sciences; approximate logarithm likelihood ratio; Background noise; cost‐reference particle filter; CRPF‐based detector; Detection, estimation, filtering, equalization, prediction; Detectors; Dynamical systems; Exact sciences and technology; Information, signal and communications theory; Manoeuvring; manoeuvring weak radar target detection; noise; nonlinear dynamic systems; Nonlinear dynamics; over‐the‐horizon radar; particle filtering (numerical methods); PF‐based detectors; piecewise parametric model; probability; radar detection; received returns; Signal and communications theory; Signal, noise; state estimation; Statistics; Target detection; target movement; Telecommunications and information theory; track‐before‐detect method; unknown background noise corruption statistics; unknown statistics; radar detection; probability; particle filtering (numerical methods); cost-reference particle filter; manoeuvring weak radar target detection; CRPF-based detector; received returns; nonlinear dynamic systems; target movement; piecewise parametric model; noise; unknown background noise corruption statistics; approximate logarithm likelihood ratio; track-before-detect method; state estimation; unknown statistics; PF-based detectors; over-the-horizon radar; Performance evaluation; Parameter estimation; Target tracking; Moving target; Probabilistic approach; Logarithmic function; Measurement sensor; Likelihood ratio; Background noise; Beyond the horizon radar; Non linear system; Algorithm; Parametric method; Algorithm performance; Probabilistic model; State estimation; Target detection; Particle filter
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2013.0117

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.