PCRLB based multisensor array management for multitarget tracking

In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of measurement origin uncertainty. There are two complicating factors: the first is that because of physical limitations (e.g., communication bandwidth) only a small number of sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. It is this second factor that extends our previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and allow accurate target state estimation. Our sensor management technique is then based on controlling the Posterior Cramer-Rao Lower Bound (PCRLB), which provides a measure of the optimal achievable accuracy of target state estimation. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we propose some approximations that reduce the computational load and we present two sensor selection strategies for closely spaced (but, resolved) targets. Simulation results show the ability of the PCRLB based sensor management technique to allow efficient utilization of the sensor resources, allowing accurate target state estimation.