The performance of matched-field track-before-detect methods using shallow-water Pacific data.

Matched-field track-before-detect processing, which extends the concept of matched-field processing to include modeling of the source dynamics, has recently emerged as a promising approach for maintaining the track of a moving source. In this paper, optimal Bayesian and minimum variance beamforming track-before-detect algorithms which incorporate a priori knowledge of the source dynamics in addition to the underlying uncertainties in the ocean environment are presented. A Markov model is utilized for the source motion as a means of capturing the stochastic nature of the source dynamics without assuming uniform motion.

On array design for matched-field processing.

Conventional plane-wave beamforming array design guidelines are motivated by the desire to obtain particular beampattern characteristics, such as main lobe width and side lobe levels. These design guidelines are appropriate for arrays employed for beamforming, where a plane-wave signal model is utilized to derive both the array design parameters and the beamforming algorithm. However, matched-field processing utilizes full-field acoustic propagation models to exploit the complexities of ocean acoustic propagation.