Distributed Computation of the Conditional PCRLB for Quantized Decentralized Particle Filters

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