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A Novel Distributed State Estimation Algorithm with Consensus Strategy.

Jun Liu1, Yu Liu2,3, Kai Dong4

  • 1Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China. 18615042187@163.com.

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|May 11, 2019
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Summary
This summary is machine-generated.

This study introduces a new distributed hybrid information weighted consensus filter (DHIWCF) for state estimation. The DHIWCF achieves good performance with limited consensus iterations, even with naive nodes.

Keywords:
Kalman filterconsensus filterdistributed state estimationmaximum a posterior estimatornaive nodesensor networks

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Area of Science:

  • Distributed systems
  • Estimation theory
  • Signal processing

Background:

  • Consensus-based paradigms are popular for distributed state estimation due to fault tolerance and scalability.
  • Existing algorithms struggle with naive nodes (nodes lacking target observation) and require many iterations, which is impractical with limited resources.
  • Limited iterations in practical applications degrade the performance of conventional algorithms.

Purpose of the Study:

  • To develop a novel distributed state estimation algorithm that performs well with few consensus iterations.
  • To address the challenge of naive nodes in distributed estimation frameworks.
  • To improve the efficiency and applicability of consensus-based algorithms in resource-constrained environments.

Main Methods:

  • Proposed a distributed local maximum a posterior (MAP) estimator by fusing local measurements and prior estimates.
  • Developed a novel distributed hybrid information weighted consensus filter (DHIWCF) by incorporating approximations and a consensus protocol.
  • Performed theoretical analysis to guarantee the stability of the DHIWCF.

Main Results:

  • The proposed DHIWCF demonstrates acceptable estimation performance even with a single consensus iteration.
  • The algorithm effectively handles naive nodes within the distributed estimation framework.
  • Simulation results validate the effectiveness and superiority of the DHIWCF compared to existing methods.

Conclusions:

  • The DHIWCF offers a robust and efficient solution for distributed state estimation, particularly in scenarios with limited communication and energy resources.
  • The algorithm's ability to perform well with minimal iterations makes it suitable for practical, real-time applications.
  • The theoretical stability guarantees further enhance the reliability of the proposed DHIWCF.