Maximum likelihood track-before-detect matched-field beamforming with SWELLEX data

Abstract

Moving source localization methods are seriously limited at low signal-to-noise ratios because the signal energy becomes spatially spread over long observation times. In this paper, the integration time available to matched-field beamformers is extended by incorporating a priori statistical knowledge of the source dynamics. This is accomplished by first showing that the well-known minimum variance beamformer output power is essentially the log-likelihood function with respect to source location under a model consisting of a single target in an unknown but structured noise field. This likelihood function for a single frame of data may be combined with the conditional distribution of the current target position given its previous location to obtain a maximum-likelihood (ML) track-before-detect processor. In cases where the sample covariance matrix is ill-conditioned, derivation of a constrained ML estimate assuming known diffuse noise level is given. Results are presented using vertical hydrophone array data for an underwater acoustic source off the coast of southern California. Comparisons with conventional MV matched-field beamforming demonstrate that incorporating source dynamics into the processor improves its ability to maintain the target track even in the presence of interference.