Research On Target Detection Method Under Non-ideal Conditions For Multistatic Radar

Distributed multistatic radars are distributed to obtain spatial diversity gain,which can effectively overcome the performance degradation of monostatic radars caused by the single observation angle and the fluctuation of target RCS.A spontaneous monostatic radar equipped with multiple receivers can detect the target cooperatively,which can effectively utilize the monostatic radar in China and lower the cost.Multistatic radar target detection not only brings the advantages of spatial diversity gain,but also brings new problems because of the non-ideal factors.On the one hand,in the detection of point targets,the coordinate system of each local receiving station is different,and the data fusion of each local station will be mismatched after conversion to the common coordinate system.At the same time,the detection performance also will be degraded because of the unknown noise power and target scattering characteristics in practice.On the other hand,when the target is a range-spread target,the signal-to-noise ratio of each scattering point is different,while the detection environment and self detection performance of each local station are different due to the sparse distribution of radar stations.Therefore,should be considered in the centralized processing of the fusion center.This thesis studies the detection problem of multistatic radar.The main work is divided into the following two aspects:Aiming at the problems of detection degradation which is due to the spatial coordinate conversion error and unknown parameters in multistatic radar detection of point target,this thesis considers to extend the only one detection unit of traditional fusion method to the observation set composed of multiple detection units,so as to increase prior information and improve detection performance.At the same time,GLRT detectors are designed to estimate the unknown parameters when the SNR of each channel is known or unknown.In the aspect of data fusion,this thesis introduces a binary parameter to fuse the detection results of other stations in the form of prior knowledge with the decisions of current stations to achieve the distributed information fusion.In order to solve the distributed detection problem of range-spread target,firstly,a signal model of range-spread target under the distance difference of each channel is established,and then the independent identically distributed conditions of the scattering coefficient of each local station is derived.Considering the difference of detection performance between different stations,this thesis proposes a method to combine the maximization of deflection coefficient with the problem of target detection.In order to improve the detection performance,we use the different weighting coefficients of each local station to influence the probability density function of the fusion center,so that the deflecion coefficient of the global detection statistics will reach the maximum.Finally,the effectiveness of the proposed algorithm is verified by simulation experiments.