A Comparison of Detection Performance for Several Track-before-Detect Algorithms. (9th December 2007)

• Record Type: Journal Article
• Authors: Davey Davey, Samuel J. Samuel J.; Rutten Rutten, Mark G. Mark G.; Cheung Cheung, Brian Brian
• Other Names: Boers Boers Yvo Yvo Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2007-12-09
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/428036
• Languages: English
• ISSNs: 1687-6172

Abstract

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.