Slow radar target detection in heterogeneous clutter using thinned space-time adaptive processing

The authors address the problem of slow target detection in heterogeneous clutter through dimensionality reduction. Traditional approaches of implementing the space-time adaptive processing (STAP) require a large number of training data to estimate the clutter covariance matrix. To address the issue of limited training data especially in the heterogeneous scenarios, they propose a novel thinned STAP through selecting an optimum subset of antenna-pulse pairs that achieves the maximum output signal-to-clutter-plus-noise ratio. The proposed strategy utilises a new parameter, named spatial spectrum correlation coefficient, to analytically characterise the effect of space-time configuration on STAP performance and reduce the dimensionality of traditional STAP. Two algorithms are proposed to solve the antenna-pulse selection problem. The effectiveness of the proposed strategy is confirmed by extensive simulation results, especially by utilising the multi-channel airborne radar measurement data set.