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Probabilistic Multi-Hypothesis Tracker with an Evolving Poisson Prior

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The Probabilistic Multi-Hypothesis Tracker (PMHT) is an efficient multi-target tracking algorithm that performs data association under a conditional independence assumption. A key part of the measurement model is the data association prior, which can be used as a track quality measure for track management decisions. The original PMHT makes this prior an unknown fixed parameter. The PMHT with hysteresis extended the measurement model by adding a Markov chain hyperparameter to the prior, but this came at the cost of exponential complexity in the number of targets. This complexity comes as a consequence of the normalization of the prior. This article shows that the PMHT data association model is equivalent to assuming that targets create a Poisson distributed number of measurements; an alternative PMHT is derived that deals directly with the Poisson model parameters and retains linear complexity in the number of targets.

Samuel J. Davey

Field of Interest

“The field of interest shall be the organization, systems engineering, design, development, integration, and operation of complex systems for space, air, ocean, or ground environments. These systems include but are not limited to navigation, avionics, mobile electric power and electronics, radar, sonar, telemetry, military, law-enforcement, automatic test, simulators, and command and control."


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