ML-PDA: Advances and a new multitarget approach

Journal on Advances in Signal Processing (Impact Factor: 0.78). 01/2008; 2008(1). DOI: 10.1155/2008/260186
Source: DBLP


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).

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Available from: P. Willett, Jul 16, 2014
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    • "It is difficult to extend ML-PDA to multiple targets [10]. While it is technically possible to write the multitarget ML-PDA log-likelihood ratio, to take into account all the joint association events the number of terms increases rapidly with the number of targets, and the expression becomes practically intractable for any more than just a few targets. "
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    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.
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    • "If the number of targets K = 2, the JLLR reduces to the form given in [12] "
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    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.
    Full-text · Article · Jan 2014 · Signal Processing
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    • "A particle filtering algorithm was developed for multiple target tracking in [9] and was applied to multitarget BOT problem. Both [6] and [9] are applicable only to scenarios where the number of targets are known. "
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    ABSTRACT: Bearings-only tracking (BOT) using a single maneuvering platform has been studied extensively in the past. However, only a few studies exist in the open literature that deal with measurement origin uncertainty. Most publications are concerned with finding the best filtering approach, since BOT is inherently nonlinear, or finding the optimal maneuver strategy for the sensor platform to improve observability. We consider measurement origin uncertainty due to the existence of multiple targets in the surveillance region, non-unity detection probability, and false alarms. Our algorithm uses the multiframe assignment (MFA) to solve the data association problem, and filtering is performed using the unscented Kalman filter (UKF). We employ both the modified and log polar coordinate systems. Simulation results show that the proposed algorithm is very effective in terms of tracking accuracy and track maintenance capability, especially when formulated in the log polar coordinate system.
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