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arXiv · 1605.04046

Track Extraction with Hidden Reciprocal Chain Models

George Stamatescu·Langford B White·Riley Bruce-Doust
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Abstract

This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generalise the familiar hidden Markov chains (HMC). HRC are able to model the "intention" of a target to proceed from a given origin to a destination, behaviour which cannot be properly captured by a HMC. While Bayesian estimation problems for HRC have previously been studied, this paper focusses principally on the problem of track extraction, of which the primary task is confirming target existence in a set of detections obtained from thresholding sensor measurements. Simulation examples are presented which show that the additional model information contained in a HRC improves detection performance when compared to HMC models.

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George Stamatescu, Langford B White, Riley Bruce-Doust. 2016-05-13. Track Extraction with Hidden Reciprocal Chain Models. https://arxiv.org/abs/1605.04046

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