Sensor management for multi-target tracking via multi-Bernoulli filtering

• Published in: Automatica (Oxford) Vol. 50; no. 4; pp. 1135 - 1142
• Main Authors: Hoang, Hung Gia, Vo, Ba Tuong
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.04.2014; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Density; Exact sciences and technology; Filtering; Management; Mathematical models; Modelling and identification; Moving targets; Multi-target tracking; Random finite sets; Sensor control; Sensors; Sequential Monte Carlo method; Stochastic systems; Tracking; Multi-target tracking; Sequential Monte Carlo method; Sensor control; Random finite sets; Target tracking; Multisensor; Moving target; Divergence; Markov decision; Filtering; Multiple target; Stochastic system; Variance; Partial observability; Uncertain system; Random set; System identification; Incomplete information; Sensor array; Particle filter
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2014.02.007

In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability.