相对传感网络分布式卡尔曼滤波器的收敛性分析

林澈; 郑荣濠; 颜钢锋; 陆诗远

doi:10.1631/FITEE.1700066

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相对传感网络分布式卡尔曼滤波器的收敛性分析

常规文章 | Updated：2022-05-19

- 相对传感网络分布式卡尔曼滤波器的收敛性分析
- Convergence analysis of distributed Kalman filtering for relative sensing networks
- 信息与电子工程前沿（英文） 2018年19卷第9期页码：1063-1075
- Affiliations：
  
  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  Zhejiang Province Marine Renewable Energy Electrical Equipment and System Technology Research Laboratory, Hangzhou 310027, China
- Author bio：
  
  [ "Che LIN, E-mail: linche@zju.edu.cn" ]
  Rong-hao ZHENG, E-mail: rzheng@zju.edu.cn
  [ "Gang-feng YAN, E-mail: ygf@zju.edu.cn" ]
  [ "Shi-yuan LU, E-mail: 917964950@qq.com" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61503335) and the Key Laboratory of System Control and Information Processing, China (No. Scip201504)
- DOI：10.1631/FITEE.1700066
  中图分类号： TP13
- 收稿：2017-01-24，
  
  修回：2018-;7-;8，
  
  网络出版：2018-09-12，
  
  纸质出版：2018-09
- Accepted：
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林澈, 郑荣濠, 颜钢锋, 等. 相对传感网络分布式卡尔曼滤波器的收敛性分析[J]. 信息与电子工程前沿（英文）, 2018,19(9):1063-1075.

Che LIN, Rong-hao ZHENG, Gang-feng YAN, et al. Convergence analysis of distributed Kalman filtering for relative sensing networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1063-1075.
林澈, 郑荣濠, 颜钢锋, 等. 相对传感网络分布式卡尔曼滤波器的收敛性分析[J]. 信息与电子工程前沿（英文）, 2018,19(9):1063-1075. DOI： 10.1631/FITEE.1700066.

Che LIN, Rong-hao ZHENG, Gang-feng YAN, et al. Convergence analysis of distributed Kalman filtering for relative sensing networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1063-1075. DOI： 10.1631/FITEE.1700066.

摘要

严格分析了相对传感网络中分布式卡尔曼滤波问题。用无向图对相对传感网络建模，并假设网络中各节点具有相同动力学模型。给出整个系统可观测性的充要条件，并详细证明。通过局部信息和测量通信，设计了一种基于卡尔曼滤波技术的分布式次优估计器，并与集中式最优估计器比较。给出系统收敛对应的网络拓扑充分条件，并结合系统参数给出

个线性矩阵不等式（linear matrix inequality

LMI）方程的数值解。数值仿真验证了所提算法的有效性。

Abstract

We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis. The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication

we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of

linear matrix inequality (LMI) equations combining system parameters. Finally

we perform several numerical simulations to verify the effectiveness of the given algorithms.

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