  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.
