Skip to content

Advertisement

  • Research Article
  • Open Access

ML-PDA: Advances and a New Multitarget Approach

EURASIP Journal on Advances in Signal Processing20072008:260186

https://doi.org/10.1155/2008/260186

  • Received: 30 March 2007
  • Accepted: 23 September 2007
  • Published:

Abstract

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).

Keywords

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

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Physical Sciences Department, York College of Pennsylvania, York, PA 17405, USA
(2)
Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Road, Storrs, CT 06269-2157, USA

Copyright

Advertisement