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The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of training data,an autoregressive(AR)-process-based covariance matrix estimator is proposed.Then,with the estimated covariance matrix the one-step generalized likelihood ratio test(GLRT) detector is designed without training data.Finally,detection performance of our proposed detector is assessed.
The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper. In most practical applications, the number of training data is limited. To overcome the lack of training data, an autoregressive (AR) -process-based covariance matrix estimator is proposed. Chen, with the estimated covariance matrix the one-step generalized likelihood ratio test (GLRT) detector is designed without training data. Finaally, detection performance of our proposed detector is assessed.