Distributed Robust State Estimation for Nonlinear Systems Based on Multivariate Laplace Distribution

WANG Guo-qing; YANG Chun-yu; MA Lei

doi:10.12263/DZXB.20220141

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Distributed Robust State Estimation for Nonlinear Systems Based on Multivariate Laplace Distribution

PAPERS | 更新时间：2025-12-08

- Distributed Robust State Estimation for Nonlinear Systems Based on Multivariate Laplace Distribution
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2610-2618(2022)
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院，江苏徐州 221116
  2.地下空间智能控制教育部工程研究中心，江苏徐州 221116
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220141
  CLC： TP24
- Received：29 January 2022，
  
  Revised：2022-04-25，
  
  Published：25 November 2022
- 稿件说明：
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王国庆,杨春雨,马磊.基于多变量Laplace分布的非线性系统分布式鲁棒状态估计[J].电子学报,2022,50(11):2610-2618.

WANG Guo-qing,YANG Chun-yu,MA Lei.Distributed Robust State Estimation for Nonlinear Systems Based on Multivariate Laplace Distribution[J].ACTA ELECTRONICA SINICA,2022,50(11):2610-2618.
王国庆,杨春雨,马磊.基于多变量Laplace分布的非线性系统分布式鲁棒状态估计[J].电子学报,2022,50(11):2610-2618. DOI： 10.12263/DZXB.20220141.

WANG Guo-qing,YANG Chun-yu,MA Lei.Distributed Robust State Estimation for Nonlinear Systems Based on Multivariate Laplace Distribution[J].ACTA ELECTRONICA SINICA,2022,50(11):2610-2618. DOI： 10.12263/DZXB.20220141.

摘要

本文考虑非高斯噪声下传感器网络的状态估计问题.在机动目标跟踪、室内定位、水声导航等应用中，传感器的非高斯噪声会造成针对高斯噪声设计的常规状态估计算法精度下降.在现有针对单传感器系统的基于多变量Laplace（Multivariate Laplace，ML）鲁棒状态估计（Robust State Estimation based on ML，RSE-ML）算法基础上，本文借助信息滤波的特点，推导了针对多传感器系统的集中式RSE-ML（Centralized RSE-ML，CRSE-ML）算法，进一步利用一致性平均得到分布式RSE-ML（Distributed RSE-ML，DRSE-ML）算法.本文提出的DRSE-ML算法中利用ML建模非高斯噪声，借助变分贝叶斯方法估计噪声和状态参数，采用一致性算法进行分布式信息交互，克服了集中式算法通信和计算负担重的缺点，且具有自由参数少、估计精度高的特点.仿真结果表明，所提出的DRSE-ML算法估计精度优于现有相关算法，且能逼近集中式CRSE-ML算法的估计精度.

Abstract

This paper considers the state estimation problem of sensor networks with non-Gaussian noise. In many applications

such as maneuvering target tracking

indoor positioning and underwater acoustic navigation

the non-Gaussian noise of the sensors may reduce the accuracy of the estimation algorithm designed for Gaussian noise. The centralized robust state estimation based on multivariate Laplace(CRSE-ML) algorithm is derived according to the characteristics of information filtering on the basis of existing RSE-ML

which is designed for single-sensor systems. Following that

the distributed RSE-ML(DRSE-ML) algorithm is then obtained with the introduction of consensus average. Within the DRSE-ML algorithm

the ML distribution is used to model non-Gaussian noise

the variational Bayesian method is applied to estimate noise and state parameters

and the consensus algorithm is adopted for distributed information exchange. The distributed algorithm overcomes the shortcomings of the centralized one

which has heavy communication and computational burden

and has few free parameters and high estimation accuracy. Simulation results show that the estimation accuracy of the proposed DRSE-ML is better than the existing related algorithms

and can approach the estimation accuracy of the centralized CRSE-ML algorithm.

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Keywords

references

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