Track Extraction with Hidden Reciprocal Chain Models
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.
Keywords
Cite
@article{arxiv.1605.04046,
title = {Track Extraction with Hidden Reciprocal Chain Models},
author = {George Stamatescu and Langford B White and Riley Bruce-Doust},
journal= {arXiv preprint arXiv:1605.04046},
year = {2016}
}