Decentralized Conditional Posterior Cramér–Rao Lower Bound for Nonlinear Distributed Estimation

Motivated by the decentralized adaptive resource management problems, the letter derives recursive expressions for online computation of the conditional decentralized posterior Cramér-Rao lower bound (PCRLB). Compared to the non-conditional PCRLB, the conditional PCRLB is a function of the past history of observations made and, therefore, a more accurate representation of the estimator's performance and, consequently, a better criteria for sensor selection. Previous algorithms to compute the conditional PCRLB are limited to centralized architectures. The letter addresses this gap. Our simulations verify the optimality of the conditional dPCRLB by comparing it with the centralized conditional PCRLB in bearing-only tracking applications.