Model-based and data-based processing of moving sources in a shallow water channel

Abstract

Adaptive MFP algorithms are applied to passively detect and localize a moving source while mitigating interference from surface shipping. A model-based motion compensation algorithm is applied to prevent smearing loss from target motion and extend the observation interval for adaptation. Moving interference is removed by applying a time-varying spatial filter at each data snapshot. The time-variation of the filter decreases the size of the instantaneous interference subspace and allows for a larger signal subspace. The interference subspace is formed by one of three methods. In the first method, knowledge of the ship position is combined with a propagation model to predict the time-varying acoustic interference. A sector null is formed to protect against mismatch degradation. In the second method, a data decomposition is used to determine the strong interferers. The third method combines model-based and data-based approaches. For each method, interference rejection is quantified by presenting SINR values for the candidate algorithm and comparing to conventional approaches. The results use data obtained from the Santa Barbara Channel Experiment. [This work was sponsored by DARPA under Air Force Contract F19628-95-C00022E. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Air Force.]