Large-Scale Optimal Sensor Array Management for Multitarget Tracking

In this paper, we are concerned with the problem of utilizing a large network of sensors in order to track multiple targets. Large-scale sensor array management has applications in a number of target tracking domains. For example, in ground target tracking, hundreds or even thousands of unattended ground sensors may be dropped over a large surveillance area. At any one time, it may then only be possible to utilize a very small number of the available sensors at the fusion center because of physical limitations, such as available communications bandwidth. A similar situation may arise in tracking sea-surface or underwater targets using a large network of sonobuoys. The general problem is then to select a small subset of the available sensors in order to optimize tracking performance. In a practical scenario with hundreds of sensors, the number of possible sensor combinations would make it infeasible to use enumeration in order to find the optimal solution. Motivated by this consideration, in this paper we use an efficient search technique in order to determine near-optimal sensor utilization strategies in real-time. This search technique consists of convex optimization followed by greedy local search. We consider several problem formulations and the posterior Cramer-Rao lower bound is used as the basis for network management. Simulation results illustrate the performance of the algorithms, both in terms of their real-time capability and the resulting estimation accuracy. Furthermore, in comparisons it can also be seen that the proposed solutions are near-optimal.