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.