Track Extraction with Hidden Reciprocal Chain Models

TL;DR

This paper introduces Bayesian track extraction algorithms using hidden reciprocal chain models, which better capture target intentions like origin-to-destination paths, leading to improved detection performance over traditional hidden Markov chains.

Contribution

It develops novel Bayesian track extraction algorithms based on hidden reciprocal chains, extending beyond hidden Markov models to incorporate target intent modeling.

Findings

HRC models improve detection accuracy over HMC models.

Simulation results demonstrate enhanced track extraction performance.

HRC effectively captures target origin-destination behavior.

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.