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The conditional posterior Cramer-Rao lower bound (PCRLB) is an effectivesensor resource management criteria for large, geographically distributedsensor networks. Existing algorithms for distributed computation of the PCRLB(dPCRLB) are based on raw observations leading to significant communicationoverhead to the estimation mechanism. This letter derives distributedcomputational techniques for determining the conditional dPCRLB for quantized,decentralized sensor networks (CQ/dPCRLB). Analytical expressions for theCQ/dPCRLB are derived, which are particularly useful for particle filter-basedestimators. The CQ/dPCRLB is compared for accuracy with its centralizedcounterpart through Monte-Carlo simulations.

Distributed Computation of the Conditional PCRLB for Quantized Decentralized Particle Filters