Bearings only tracking in clutter with observer control using the Bernoulli particle filter

The paper deals with autonomous bearings-only tracking of a single appearing/disappearing target in the presence of detection uncertainty (false and missed detections) with observer control. The optimal tracking method for this problem in the sequential Bayesian estimation framework is the Bernoulli filter. Observer control is based on previously acquired measurements and is formulated as a partially observable Markov decision process (POMDP). Future observer actions are ranked according to their associated reward formulated as an information theoretic criterion. The paper develops a particle filter implementation of both the Bernoulli filter and the action reward