Consensus-Based Distributed Filtering with Fusion Step Analysis
Jiachen Qian, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi

TL;DR
This paper analyzes the performance of consensus-based distributed filtering with limited fusion steps, providing a theoretical framework for understanding the trade-offs between filtering accuracy and communication costs in sensor networks.
Contribution
It introduces a novel analysis of finite fusion steps in distributed filtering, deriving explicit relations between performance degradation and fusion frequency.
Findings
Performance degradation can be quantified in closed form.
Steady-state covariance converges exponentially to centralized optimal.
Trade-off between communication cost and filtering accuracy is established.
Abstract
For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering. However, due to the limited communication resources in physical systems, the number of fusion steps cannot be infinite. To deal with this issue, the present paper analyzes the performance of CMDF with finite consensus fusion operations. First, by introducing a modified discrete-time algebraic Riccati equation and several novel techniques, the convergence of the estimation error covariance matrix of each sensor is guaranteed under a collective observability condition. In particular, the steady-state covariance matrix can be simplified as the solution to a discrete-time Lyapunov equation. Moreover, the performance degradation induced by reduced…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Target Tracking and Data Fusion in Sensor Networks · Distributed Sensor Networks and Detection Algorithms
