  The paper addresses the problem of multi-sensor control for multi-target
tracking via labelled random finite sets (RFS) in the sensor network systems.
Based on an information theoretic divergence measure, namely Cauchy-Schwarz
(CS) divergence which admits a closed form solution for GLMB densities, we
propose two novel multi-sensor control approaches in the framework of
generalized Covariance Intersection (GCI). The first joint decision making
(JDM) method is optimal and can achieve overall good performance, while the
second independent decision making (IDM) method is suboptimal as a fast
realization with smaller amount of computations. Simulation in challenging
situation is presented to verify the effectiveness of the two proposed
approaches.
