Adaptive Detection Of Target With MIMO Radar

The ability of target detection for radar is being challenged by the various and complex environments.Different from the traditional monostatic radar and phase array radar,the multiple-input multiple-output(MIMO)radar exploits multiple transmitters and receivers to observe target in a more comprehensive manner and obtain more detailed target information that can improve the radar performance in target detection,and has raised much attention internationally in the research area of radar signal processing.The targets are usually overwhelmed by clutters,and the utilization of adaptive technique makes the detectors match the clutter environment better,which is the key to improve target detection performance.In this thesis,the problem of adaptive target detection with MIMO radar is studied.The main contributions of this thesis are as follows:1.In order to solve the problem of limited secondary data,the adaptive algorithms based on Bayesian theory are studied for target detection with MIMO radar under the background of compound-Gaussian clutter.By exploiting the a priori distribution knowledge of the clutter,several two-step adaptive generalized likelihood ratio tests(GLRTs)based on the Bayesian theory are proposed.For different clutter textures,several maximum a posteriori estimators of the clutter covariance matrix structure are derived.The Bayesian based algorithms achieve good adaptive target detection performance in the case of limited secondary data.2.In order to solve the problem of limited secondary data,an adaptive algorithm based on multiple a priori spectral models is studied for target detection with MIMO radar under the background of compound-Gaussian clutter.It is supposed that some a priori spectral models for the clutter are available.The actual clutter inverse covariance matrix structure is modeled as a combination of these available a priori models.Then the estimation problem of clutter matrix parameters is transformed successfully into the estimation problem of a priori models parameters.A one-step GLRT is proposed by exploiting an iterative procedure to evaluate the constrained maximum likelihood estimations of the unknown parameters.The proposed detector achieves excellent adaptive detection performance when a limited number of secondary data is available.3.The problem of adaptive moving target detection with MIMO radar in the compound-Gaussian clutter environment is studied.The new moving target detectors based on GLRT,Rao and Wald criteria are proposed using an ad-hoc design procedure.For the case of limited secondary data,a class of covariance matrix estimators based on the geometric barycenters is proposed.The proposed detectors and covariance matrix estimators are effective for moving target detection and estimation in the compound-Gaussian clutter.4.The problem of adaptive moving target detection with polarimetric MIMO radar in the presence of Gaussian clutter is studied.In the polarimetric framework,the GLRT for moving target is proposed.Then,the estimation methods of target velocity and clutter covariance matrix are investigated.By exploiting polarization information,the detection performance of moving target,and the estimation performance of target velocity and clutter covariance matrix are significantly improved.