ML-PDA: Advances and a New Multitarget Approach.
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: We present two procedures for validating track estimates obtained using the maximum-likelihood probabilistic data association (ML-PDA) algorithm. The ML-PDA, developed for very low observable (VLO) target tracking, always provides a track estimate that must then be tested for target existence by comparing the value of the log likelihood ratio (LLR) at the track estimate to a threshold. Using extreme value theory, we show that in the absence of a target the LLR at the track estimate obeys approximately a Gumbel distribution rather than the Gaussian distribution previously ascribed to it in the literature. The offline track validation procedure relies on extensive offline simulations to obtain a set of track validation thresholds that are then used by the tracking system. The real-time procedure uses the data set that produced the track estimate to also determine the track validation threshold. The performance of these two procedures is investigated through simulation of two active sonar tracking scenarios by comparing the false and true track acceptance probabilities. These techniques have potential for use in a broader class of maximum likelihood estimation problems with similar structureIEEE Transactions on Signal Processing 06/2007; · 2.81 Impact Factor
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ABSTRACT: David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.Addison-Wesley, Reading, Massachusetts. 01/1989;
Conference Proceeding: A comparison of particle filters for recursive track-before-detect[show abstract] [hide abstract]
ABSTRACT: Track-before-detect is a powerful technique for detection and tracking of targets with low signal-to-noise ratio. This paper presents a performance comparison of two particle filters recently proposed for this application using several different particle proposal densities designed for track initiation. The first particle filter is a standard SIR particle filter, which treats the track-before-detect problem as a hybrid estimation problem by incorporating a discrete random variable, "target existence," into the state vector. The second particle filter formulates the probability of existence calculation in a different way, avoiding the need for hybrid estimation. Three different particle proposal densities are considered, which are designed to compare performance when the data is used to aid in particle proposal.Information Fusion, 2005 8th International Conference on; 08/2005