Analytical Performance Bounds for Full and Reduced-order Distributed Bayesian Estimation
نویسنده
چکیده
Motivated by the resource management problem in nonlinear multi-sensor tracking networks, the paper derives online, distributed estimation algorithms for computing the posterior Cramér-Rao lower bound (PCRLB) for full-order and reduced-order distributed Bayesian estimators without requiring a fusion centre 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. IEEEkeywords: Cramér-Rao bounds, Data fusion, Distributed estimation, dPCRLB, Multi-sensor tracking, Nonlinear systems, Particle filters, and PCRLB.
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