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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Tantum, SL; Nolte, LW; Krolik, JL; Harmanci, K

Published Date

  • July 2002

Published In

Volume / Issue

  • 112 / 1

Start / End Page

  • 119 - 127

PubMed ID

  • 12141336

Pubmed Central ID

  • 12141336

Electronic International Standard Serial Number (EISSN)

  • 1520-8524

International Standard Serial Number (ISSN)

  • 0001-4966

Digital Object Identifier (DOI)

  • 10.1121/1.1489435

Language

  • eng