Reduced-rank adaptive detection of distributed sources using subarrays

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. 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 data samples. One would expect that processing on subarrays may result in performance loss in that smaller number of degrees of freedom is utilized. However, it also leads 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 detectors have a significant gain over the full array detector. In addition, the subarray processing can also be successfully applied to the problem of detecting moving sources in an underwater acoustic scenario. We validate our results by computer simulations.