Full- and reduced-order distributed Bayesian estimation analytical performance bounds

Motivated by the resource management problem in nonlinear multisensor tracking networks, the paper derives online, distributed estimation algorithms for computing the posterior Cramer-Rao lower bound (PCRLB) for full-order and reduced-order distributed Bayesian estimators without requiring a fusion center and with nodal communications limited to local neighborhoods. For both cases, Riccati-type recursions are derived that sequentially determine the global Fisher information matrix (FIM) from localized FIMs of the distributed estimators. We use particle filter realizations for these bounds and quantify their performance for data fusion problems through Monte Carlo simulations.