Performance Analysis of Subperture Processing Using a Large Aperture Planar Towed Array

Jennifer Watson, Ph.D., 2004
Arthur Baggeroer, Advisor

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 multipath propagation with high transmission loss. Large aperture planar arrays have the potential to improve detection performance with their high resolution and high gain, but are susceptible to two 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 using large arrays. This thesis develops statistical models for the detection performance of three adaptive, sample-covariance-based subarray processing algorithms which incorporate the effects of limited spatial coherence and snapshot support. The performance of the optimum processor conditioned on known data covariances is derived as well for comparison. These models are used to compare subarray algorithms and partitioning schemes in a variety of environments using several propagation models. The analysis shows a tradeoff between the required adaptive degrees of freedom, snapshot support, and adaptive resolution. This thesis shows that for both plane-wave and matched-field processing, the Conventional-Then-Adaptive (CTA) algorithm optimizes this tradeoff most efficiently.