Space-time interpolation for adaptive arrays with limited training data

This paper describes a method for improving the small sample support space-time adaptive processing (STAP) performance of distorted linear arrays. Receive arrays which deviate from a straight line may occur, for example, in conformal radar and towed sonar array applications. With limited training data, distorted linear arrays suffer greater signal to interference plus noise (SINR) loss due to inflation of the clutter covariance matrix rank. In this paper, Brennan's rule for the clutter covariance matrix rank is extended to 2D arrays and used to motivate the development of a space-time interpolation (STINT) method for clutter rank reduction. By using a space-time transformation that minimizes the constrained mean-square-error between clutter at the distorted array and a virtual uniform line array, STINT processing lowers the clutter covariance rank and hence improves output SINR when training data is limited. Simulation results also indicate that STINT processing reduces the minimum detectable target velocity (MDV) achievable by finite sample support STAP.