PROJECT TITLE:

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

ABSTRACT:

The authors address the matter of slow target detection in heterogeneous muddle through dimensionality reduction. Ancient approaches of implementing the house-time adaptive processing (STAP) need a giant number of training information to estimate the muddle covariance matrix. To address the difficulty of restricted coaching information particularly within the heterogeneous scenarios, they propose a unique thinned STAP through selecting an optimum subset of antenna-pulse pairs that achieves the most output signal-to-litter-plus-noise ratio. The proposed strategy utilises a replacement parameter, named spatial spectrum correlation coefficient, to analytically characterise the impact of house-time configuration on STAP performance and reduce the dimensionality of ancient STAP. 2 algorithms are proposed to resolve the antenna-pulse selection problem. The effectiveness of the proposed strategy is confirmed by extensive simulation results, particularly by utilising the multi-channel airborne radar measurement knowledge set.