A Comparison of Detection Performance for Several Track-before-Detect Algorithms

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data...

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Main Authors: Brian Cheung, Mark G. Rutten, Samuel J. Davey
Format: Article
Language:English
Published: SpringerOpen 2007-12-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2008/428036
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author Brian Cheung
Mark G. Rutten
Samuel J. Davey
author_facet Brian Cheung
Mark G. Rutten
Samuel J. Davey
author_sort Brian Cheung
collection DOAJ
description A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.
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spelling doaj.art-c433ee59edc0464cafe238b1c25e05762022-12-21T18:59:22ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61722007-12-01200810.1155/2008/428036A Comparison of Detection Performance for Several Track-before-Detect AlgorithmsBrian CheungMark G. RuttenSamuel J. DaveyA typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.http://dx.doi.org/10.1155/2008/428036
spellingShingle Brian Cheung
Mark G. Rutten
Samuel J. Davey
A Comparison of Detection Performance for Several Track-before-Detect Algorithms
EURASIP Journal on Advances in Signal Processing
title A Comparison of Detection Performance for Several Track-before-Detect Algorithms
title_full A Comparison of Detection Performance for Several Track-before-Detect Algorithms
title_fullStr A Comparison of Detection Performance for Several Track-before-Detect Algorithms
title_full_unstemmed A Comparison of Detection Performance for Several Track-before-Detect Algorithms
title_short A Comparison of Detection Performance for Several Track-before-Detect Algorithms
title_sort comparison of detection performance for several track before detect algorithms
url http://dx.doi.org/10.1155/2008/428036
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