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 clutter. There are three complicating factors. The first is that because of physical limitations (e.g., communication bandwidth) only a small subset of the available sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. The third complication is that the total number of targets in the surveillance region is unknown and possibly time varying. It are these second and third factors that extend previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and to allow accurate estimation of the states of a varying number of targets. We, pose the problem as a bi-criterion optimization with the two objectives of 1) controlling the posterior Cramer-Rao lower bound ((PCRLB) which provides a measure of the optimal achievable accuracy of target state estimation), and 2) maximizing the probability of detecting new targets. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we show the existence of a multitarget information reduction matrix (IRM) which can be calculated off-line in most cases. Additionally, we propose some approximations that further reduce the computational load. We present solution methodologies that, in simulations, are shown to determine efficient utilization strategies for the available sensor resources, with some sensors selected to track existing targets and others given the primary task of surveillance in order to identify new threats.