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).

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    ABSTRACT: The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker are both simple, straightforward algorithms that can be used in an active multistatic sonar framework. With some basic assumptions about a target (or targets) as well as the environment, likelihood ratios can be developed for both algorithms and then optimized. The main difference between the two algorithms is in the target assignment model; ML-PDA assumes that at most one measurement per scan can originate from a target, while ML-PMHT allows for any number of measurements to have originated from a target. While this assumption may reduce the appeal of the ML-PMHT, the resulting algorithm does offer advantages in both its implementation (especially fine-scale optimization) and in terms of its multitarget formulation. The algorithms were tested with Monte Carlo trials on five different synthetic multistatic sonar scenarios. These scenarios were designed to test a range of geometries, including a single target, closely-spaced targets, crossing targets, large numbers of targets, and large numbers of sources and receivers.
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    ABSTRACT: This article presents a Bayesian algorithm for detection and tracking of a target using the track-before-detect framework. This strategy enables to detect weak targets and to circumvent the data association problem originating from the detection stage of classical radar systems. We first establish a Bayesian recursion, which propagates the target state probability density function. Since raw measurements are generally related to the target state through a nonlinear observation function, this recursion does not admit a closed form expression. Therefore, in order to obtain a tractable formulation, we propose a Gaussian mixture approximation. Our targeted application is passive radar, with civilian broadcasters used as illuminators of opportunity. Numerical simulations show the ability of the proposed algorithm to detect and track a target at very low signal-to-noise ratios.
    Journal on Advances in Signal Processing 2013(1). · 0.81 Impact Factor
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    ABSTRACT: This work investigates the various criteria for track-to-track association/fusion (T2TA/F): likelihood ratios and distance criteria. Procedures to obtain the quantities needed by the LR criterion from the limited information available from the real world communication networks are developed. Algorithms for T2TA/F with heterogeneous sensors and investigation of several assignment algorithms for the T2TA problem are carried out. Procedures for simultaneous handling of continuous valued (kinematic and feature) states and discrete valued ones (attribute/classification) for an integrated approach to the Track Association and Fusion problem are presented.

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