Distributed Computation of the Conditional PCRLB for Quantized Decentralized Particle Filters

TL;DR

This paper introduces a new distributed method to compute the conditional PCRLB in quantized decentralized sensor networks, reducing communication overhead and improving estimation efficiency for particle filter-based systems.

Contribution

It derives analytical expressions for the conditional PCRLB in quantized decentralized networks, enabling more efficient distributed sensor resource management.

Findings

Analytical expressions for CQ/dPCRLB are derived.

CQ/dPCRLB closely matches centralized PCRLB in accuracy.

Proposed method reduces communication overhead in sensor networks.

Abstract

The conditional posterior Cramer-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.