3D adaptive matched field processing for a moving source in a shallow water channel

In this paper we demonstrate passive detection and localization in a noisy shallow water environment using matched field processing (MFP) with data obtained during the Santa Barbara Channel Experiment (SBCX). The use of an Adaptive MFP algorithm provides the ability to detect a submerged source in the presence of strong surface interference and also reduces ambiguity surface sidelobe clutter. We also consider the effects of source motion during the observation interval. Target motion can degrade signal gains by introducing, smearing across MFP range cells. For large arrays, such as those deployed during SBCX, motion can also introduce errors due to differential doppler across the array. Since long observation times are desirable for increased noise gain and to provide sample support for the adaptive algorithms, motion compensation is required. An approach is described that uses a velocity hypothesis to focus the snapshots prior to covariance estimation. Results show that with compensation, localization accuracy is improved and the full resolution of the array can be realized