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Tracking with Deterministic Batch Trackers

  • Steven SchoeneckerEmail author
Chapter
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Part of the STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health book series (STEAM)

Abstract

In this chapter, we present two deterministic (i.e., non-Bayesian) batch trackers—the Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT). Both trackers formulated by making assumptions about the target and the environment in which the target is present. Using these assumptions, in both cases, a log-likelihood ratio (LLR) is formulated, and then the state vector x that maximizes this LLR is usually chosen as the target state. Both the ML-PDA and the ML-PMHT LLRs are developed. We specifically consider two different amplitude likelihood ratios that have been used in these trackers—a fluctuating Gaussian model and a heavier-tailed clutter model. Finally, we present a method for determining a “tracking threshold” for ML-PMHT—i.e., if the maximum ML-PMHT likelihood value for a batch of data is above this level, it is determined to be a target; if it is below this level, the peak is rejected as clutter originated.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Naval Undersea Warfare CenterNewportUSA

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