Sparse Sensing for Target Detection

In this chapter, we investigate the problem of slow target detection in heterogeneous clutter in the context of radar space-time adaptive processing (STAP). Traditional STAP approaches require a large number of training data to estimate the clutter covariance matrix, which prohibits their practical applications. In order to address the issue of limited training data especially in the heterogeneous scenarios, we propose a novel thinned STAP via selecting an optimum subset of antenna-pulse pairs in the metric of maximum output signal-to-clutter-plus-noise ratio (SCNR). The proposed thinned STAP strategy defines a new parameter, named spatial spectrum correlation coefficient (S \(^2\) C \(^2\) ), to analytically characterize the effect of space-time configuration on STAP performance and reduce the dimensionality of traditional STAP. Three algorithms are proposed to solve the antenna-pulse pair selection problem.