Passive Coherent Location For Low Observable Targets

With the rapid development of stealth and low altitude penetration techniques,the surveillance background of radar becomes more and more complicated.The active radar system has to face the challenge of detecting and tracking the low observable targets in heavy clutter.Compared with the traditional active radar systems,the passive coherent location(PCL)system has small size,anti-interference and strong ability to survive.Also,the spatial distribution characteristics of the PCL can greatly improve the detecting performance for low observable targets.Consequently,the PCL has important theoretical significance and military value.In order to improve the detection performance of PCL for low observable targets,several detecting algorithms are proposed in this thesis.The main contributions are summarized as follows:Firstly,in order to track a single low observable target with a bistatic PCL system,a simulated annealing maximum likelihood probabilistic data association(SA-ML-PDA)algorithm is proposed.In this algorithm,the ML-PDA method is presented for track initialization,and the SA algorithm is used for optimization.In addition,the track maintenance is achieved in a sliding window manner.Simulation results verify the effectiveness of the proposed algorithm.Secondly,a genetic algorithm maximum likelihood probabilistic multi-hypothesis method is proposed to improve the detecting performance with a multistatic PCL system.Also,the multi-sensor information fusion technique is used to improve the estimation performance.Compared with traditional methods,the proposed method can greatly improve the tracking performance.Finally,in order to detect and track multiple low observable targets with a bistatic PCL system,where the number of targets is unknown,a quasi-Monte Carlo simulated annealing maximum likelihood probabilistic multi-hypothesis(QS-ML-PMHT)algorithm is proposed.The ML-PMHT method is presented for multi-target track initialization and determining the number of targets.In addition,the QS technique is used for optimization and hence improving the estimation performance.Simulation results verify the effectiveness of the proposed algorithm.