Adaptive subarray processing for distributed sources

We introduce a framework for exploring array detection problems in a reduced dimensional space. This involves calculating a structured subarray transformation matrix for the detection of a distributed signal using large aperture linear arrays for short data records. We study the performance of the adaptive subarray detector and evaluate its potential improvement in detection performance compared with the full array detector with finite samples. One would expect that processing on subarrays may result in performance loss in that smaller number of degrees of freedom is utilized, yet lead to a better estimation accuracy for the interference and noise covariance matrix with finite data samples, which will yield some gain in performance. By studying the subarray detector for general linear arrays, we identify this gain under various scenarios. We show that when the number of samples is small, the subarray detector has a significant performance gain over the full array detector. We validate our results by computer simulations.