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