Posterior Cramér-Rao Lower Bound for Multiple Passive Sensors in an Uncertain Ocean Environment

Abstract

In this paper, our study is motivated by the fact that it is not always clear what the placement of the multiple passive sensors giving the best tracking performance for the underwater targets of interest might be. To account for the issue, posterior Cramer-Rao lower bound (PCRLB) is utilized, which provides a measure of the optimal achievable accuracy of the target state estimation. To derive the recursive Fisher information matrix (FIM) and PCRLB for multisensor multitarget state estimation in an uncertain ocean environment, we address the impact of the uncertain propagation, which is ignored by the previously researches. It is demonstrated that the propagation uncertainty and target tracking results play important roles in the FIM and PCRLB. Moreover, the general framework for integrated target tracking and sensor placement is also proposed.