In recent years the focus of passive detection and localization of submarines has moved from the deep ocean into the littoral regions. the problem of passive detection in these regions is complicated by strong multipath propagation with high transmission loss. Large aperture planar arrays have the potential to improve detection performance due to their high resolution and high gain, but are suceptible to two main performance degradation mechanisms: limited spatial coherence of signals and nonstationarity of high bearing rate interference sources common in littoral regions of strategic importance. This thesis presents subarray processing as a method of improving passive detection performance using such large arrays. This thesis develops statistical models for the detection of performance of three adaptive, sample-covariance-based subarray processing algorithms which incorporate the effects of limited spatial coherence as well as finite snapshot support. The performance of the optimum processor conditioned on known data coveriances is derived as well for comparison. These models are then used to compare subarray algorithms and partitioning schemes in a variety of interference environments using plane wave and matched-field propagation models.
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