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. In addition, the relationship between optimal Bayesian track-before-detect processing and minimum variance track-before-detect beamforming is examined, revealing how an optimal tracking philosophy may be used to guide the modification of existing beamforming techniques to incorporate track-before-detect capabilities. Further, the benefits of implementing an optimal approach over conventional methods are illustrated through application of these methods to shallow-water Pacific data collected as part of the SWellEX-1 experiment. The results show that incorporating Markovian dynamics for the source motion provides marked improvement in the ability to maintain target track without the use of a uniform velocity hypothesis.
Department of Electrical Engineering, Duke University, Durham, North Carolina 27708-0291, USA. slt@ee.duke.edu
Stacy L
SL
Tantum
Loren W
LW
Nolte
Jeffrey L
JL
Krolik
Kerem
K
Harmanci
D000162:Acoustics; D001499:Bayes Theorem; D009792:Oceans and Seas; D010140:Pacific Ocean