Multiple Hypotheses AMF,Rao and Wald Detection Method Under Subspace Uncertainty
Since the uncertainty in the size of the target multi-rank subspace will lead to multiple hypotheses detection,the traditional target adaptive binary detection method is no longer applicable.To address this issue,a multiple hypothesis AMF,Rao and Wald detection method under subspace uncertainty is proposed.Firstly,based on the Kullback-Leibler in-formation criterion,a target detection model under multiple assumptions in the target multi-rank subspace is established.Then,based on the AMF,Rao and Wald detection criteria,multiple hypotheses detectors are designed,and the unknown parameters are optimized and the penalty term is calculated.Finally,the performance of the proposed detectors are veri-fied by simulation experiments,and the influence of the penalty term on the performance of each detector is analyzed.The experimental results show that compared with the traditional detector,the proposed detectors have better detection performance under certain conditions.
adaptive target detectionmulti-rank subspacemultiple hypotheses detectionmodel order selection
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