MIMO Radar Detection in Compound Gaussian Sea Clutter Using Joint Model and Sample Selection

Abstract

This paper deals with the adaptive detection of moving target for multiple-input multiple-output (MIMO) radar in compound Gaussian sea clutter circumstances. A novel detector based on two-step generalized likelihood ratio test (GLRT) framework is developed by using model and sample selection (MSS) algorithm. Specifically, the type of detector is determined by the model selection (MS) approach according to the minimum of model fitting error using the Anderson-Darling (AD) test, and then the better secondary data are chosen from reference cells based on the samples selection (SS) criterion. Finally, the proposed detector on the basis of MSS algorithm is compared with traditional detectors using the real sea clutter data, i.e., IPIX data. The numerical results indicate that the novel MSS algorithm for detector exhibit more significant performances than the traditional detectors.