Covariance Matrix Reconstruction Using Parsimonious Measurements and Low-sample Support

In this paper, we consider the problem of space-time adaptive processing (STAP) weight vector design using parsimonious spatial measurements and low temporal sample support. The extreme case when a single space-time data snapshot is the only available data is considered. It is assumed that dense discrete clutter components are spread along the clutter ridge. We propose a method for clutter-plus-noise covariance matrix reconstruction in the absence of secondary data. A two stage approach is adopted where a coarse angle-Doppler map is created in the first stage while a fine map is obtained in the second stage. It is shown that the clutter components can be accurately localized in the final angle-Doppler map. The final map is used to construct the covariance matrix and design the full array STAP weight vector. We show that the performance of the proposed STAP processor using parsimonious measurements is comparable to the performance of STAP design using full-dimensional arrays. Simulations examples are used to validate the effectiveness of the proposed STAP design technique.