Research On Signal Integration And Detection Algorithm For High-Speed Target Of Bistatic MIMO Radar

The emergence of high-speed targets with low observability poses a great challenge to the detection performance of traditional single radar node.Bistatic multiple input multiple output(MIMO)radars can effectively improve the signal-to-noise ratio of echoes and the detection performance of radar systems for high-speed targets,owing to the signal-level fusion integration of multi-channel echoes(including intra-channel echo integration and inter-channel echo integration).However,due to the complex motion characteristics between high-speed targets and multiple radar nodes,the range migration of intra-channel echoes and the difference in envelope and phase characteristics of interchannel echoes can make the integration process difficult to implement.In view of the above problems,this thesis carries out the research on signal integration and detection algorithm for high-speed target of the bistatic MIMO radar.The main contents are as follows:1.The high-speed target echo model of bistatic MIMO radar is established,the transmitted signal characteristics and received echo characteristics of radar nodes are analyzed,and the basic theory and method of signal integration and detector design are discussed,which lays a theoretical foundation for the design of subsequent correlation algorithms.2.A signal integration and detection algorithm based on modified Radon-Fourier transform-cuckoo search(MRFT-CS)is analyzed.Firstly,the multi-channel echoes are obtained by pulse compression.Secondly,the range migration in the echoes is compensated by MRFT,and then the intra-channel echoes are coherently integrated.Thirdly,the difference in envelope characteristics between the multi-channel echoes is compensated by CS,and then the inter-channel echoes are coherently integrated.Finally,the target detection and parameter estimation are completed.3.When the target distance and velocity information is not sufficient,the integration performance of MRFT-CS algorithm will decrease.A signal integration and detection algorithm based on complex-valued deep neural networks-particle swarm optimization(CVDNN-PSO)is analyzed.Firstly,the multi-channel echoes are obtained by pulse compression.Secondly,CVDNN is used to deduce the target track of the intra-channel echoes,and the target vectors are extracted along the reasoning tracks.Thirdly,the phase characteristic difference between the multi-channel target vectors is compensated by PSO,and the joint coherent integration between the intra-channel echoes and the inter-channel echoes is realized.Finally,the target detection and parameter estimation are completed.Numerical simulation experiments show that the proposed algorithms have good detection performance and can effectively realize signal-level fusion integration of multichannel echoes.