Passive Coherent Location Based On Random Finite Set

With the increasing development of modern fighter and stealth technology,traditional active radars face severe challenges.Their performance of detection,anti-interference,and safety decreased rapidly.The Passive Coherent Location(PCL)system,which exploits non-cooperative illuminators of opportunity,has a strong anti-interference ability and good stealth performance since no electromagnetic wave is emitted from the radar system.Consequently,the PCL system is of important research significance in military defense field and attracts lots of attentions.In this thesis,the Random Finite Set(RFS)based multi-target passive coherent location problem is mainly concerned,and the contributions are given as follows:1.A Gaussian Mixture Probability Hypothesis Density(GMPHD)filter is proposed for multi-target tracking using bi-static PCL system,where the clutter density is known a priori.First,the multi-target states and multi-target measurements of bi-static PCL system are expressed as two RFSs.Second,the GMPHD filter together with the unscented transform is used to predict and update the multi-target intensity.Finally,the target number and the state of each target are estimated using the posterior intensity composed of multiple Gaussian components.Simulation results verify the effectiveness of the proposed method.2.In order to address the problem of tracking multiple low observable targets with multi-static PCL system under known clutter background,a superimposed intensity multi-static GMPHD passive coherent location algorithm is proposed.First,a gate technique is used to choose valid station for each predicted Gaussian component.Second,the predicted Gaussian components are updated with measurements from valid stations,and the local posterior intensity of each station is obtained.Then the fusion intensity is obtained by summing local posterior intensities,as well as the miss-detection intensity.Last,a two-step strategy is proposed to extract targets states.The proposed algorithm can effectively improve the detection and tracking performance of multi-static PCL system for low observable targets.3.In order to solve the problem of multi-target passive coherent location in clutter with unknown density,a multi-scan clutter sparsity estimation and GMPHD based multi-target passive coherent location algorithm is proposed.First,a feedback model that connecting the Gaussian mixture posteriori intensity with the clutter density estimation is constructed.The potential target-originated measurements are eliminated by a designed threshold,which helps to reduce the effect on the clutter density estimation of the target-originated measurements.Second,a multi-scan clutter sparsity estimator is proposed to estimate the nonuniform clutter density online,that can improve the tracking performance with unknown clutter density.