- Research Article
- Open Access
ML-PDA: Advances and a New Multitarget Approach
EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 260186 (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).
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Blanding, W., Willett, P. & Bar-Shalom, Y. ML-PDA: Advances and a New Multitarget Approach. EURASIP J. Adv. Signal Process. 2008, 260186 (2007). https://doi.org/10.1155/2008/260186
- Computational Efficiency
- Target Detection
- Tracking Algorithm
- Detection Processing
- Target Tracking