Sensor Selection with Correlated Measurements for Target Tracking in Wireless Sensor Networks

We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alternative conditional posterior Cramer-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.