Article

ML-PDA: Advances and a New Multitarget Approach.

Journal on Advances in Signal Processing (Impact Factor: 0.81). 01/2008; 2008. DOI: 10.1155/2008/260186
Source: DBLP

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 (SNR) 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 multi-target tracker and compare its performance to that of the Probabilistic Multi-Hypothesis Tracker (PMHT).

Full-text

Available from: P. Willett, Jul 16, 2014
1 Follower
 · 
96 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Target tracking in high clutter or low signal-to-noise ratio (SNR) environments is an important topic and still a challenging task. Joint Maximum Likelihood Probabilistic Data Association (JML-PDA) is a well-known batch method for initializing the tracks of very low observable (VLO) targets in heavy clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for recursive track maintenance, lacks track initialization capability. In this paper, we propose a Combined JML-PDA and JPDA (CJML-PDA) algorithm to automatically initialize and maintain the tracks. This combined approach seamlessly shares information between the initialization and maintenance stages of the tracker. In contrast, in other batch-recursive approaches the initialization and maintenance algorithms operate rather independent of each other. The effectiveness of the proposed algorithm is demonstrated on a heavy clutter scenario and its performance is tested on closely-spaced (but resolved) targets with association ambiguity using angle-only measurements.
    Signal Processing 01/2014; 94:241-250. DOI:10.1016/j.sigpro.2013.06.026 · 2.24 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The maximum-likelihood probabilistic data association (ML–PDA) tracker and the maximum-likelihood probabilistic multihypothesis (ML–PMHT) tracker are tested in their capacity as algorithms for very low observable (VLO) targets (meaning 6-dB postsignal processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources, and multiple receivers. Both methods end up performing well in situations where there is a single target or widely spaced targets. However, ML–PMHT has an inherent advantage over ML–PDA in that its likelihood ratio (LR) has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. This formulation, presented here for the first time, gives ML–PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.
    IEEE Journal of Oceanic Engineering 04/2014; 39(2):303-317. DOI:10.1109/JOE.2013.2248534 · 1.33 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A robust particle filter (PF)-based multi-target tracking solution for passive sonar systems able to track an unknown time-varying number of multiple targets, while keeping continuous tracks of such targets, is presented in this article. PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics and noise distributions. An enhanced version of PF is employed and is called Mixture PF. The commonly used sampling/importance resampling PF samples from the prior importance density, while Mixture PF samples from both the prior and the observation likelihood. In order to be able to track an unknown time-varying number of multiple targets, two Mixture PFs are used, one for target detection and the other for tracking multiple targets, and a density-based clustering technique is used after the first filter. This article demonstrates the applicability of the proposed technique for the passive problem, which suffers from the lack of measurements and the small detection range of the buoys, especially for weak signals. A contact-level simulation was used to generate different scenarios and the performance of the proposed technique called Clustered-Mixture PF was examined with either bearing measurement only or bearing and Doppler measurements, and it demonstrated its high performance.
    International Journal of Systems Science 03/2014; 45(3). DOI:10.1080/00207721.2012.724097 · 1.58 Impact Factor