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  • Research Article
  • 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:


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 Efficiency
  • Target Detection
  • Tracking Algorithm
  • Detection Processing
  • Target Tracking

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Authors’ Affiliations

Physical Sciences Department, York College of Pennsylvania, York, PA 17405, USA
Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Road, Storrs, CT 06269-2157, USA