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Open Access

ML-PDA: Advances and a New Multitarget Approach

EURASIP Journal on Advances in Signal Processing20072008:260186

Received: 30 March 2007

Accepted: 23 September 2007

Published: 6 November 2007


Developed over 15 years ago, the maximum-likelihood-probabilistic data association target tracking algorithm has been demonstrated to be effective in tracking very low observable (VLO) targets where target signal-to-noise ratios (SNRs) require very low detection processing thresholds to reliably give target detections. However, this algorithm has had limitations, which in many cases would preclude use in real-time tracking applications. In this paper, we describe three recent advances in the ML-PDA algorithm which make it suitable for real-time tracking. First we look at two recently reported techniques for finding the ML-PDA track estimate which improves computational efficiency by one order of magnitude. Next we review a method for validating ML-PDA track estimates based on the Neyman-Pearson lemma which gives improved reliability in track validation over previous methods. As our main contribution, we extend ML-PDA from a single-target tracker to a multitarget tracker and compare its performance to that of the probabilistic multihypothesis tracker (PMHT).


Computational EfficiencyTarget DetectionTracking AlgorithmDetection ProcessingTarget Tracking

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Authors’ Affiliations

Physical Sciences Department, York College of Pennsylvania, York, USA
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, USA


© Wayne Blanding 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.