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Open Access

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

EURASIP Journal on Advances in Signal Processing20072008:428036

https://doi.org/10.1155/2008/428036

Received: 30 March 2007

Accepted: 8 October 2007

Published: 15 November 2007

Abstract

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.

Keywords

Dynamic ProgrammingPoint MeasurementQuantum InformationDetection AlgorithmSensor Data

Publisher note

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Authors’ Affiliations

(1)
Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, Australia

Copyright

© Samuel J. Davey et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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