基于动态加权的量化分布式卡尔曼滤波

doi:10.11772/j.issn.1001-9081.2015.07.1824

计算机应用 ›› 2015, Vol. 35 ›› Issue (7): 1824-1828.DOI: 10.11772/j.issn.1001-9081.2015.07.1824

基于动态加权的量化分布式卡尔曼滤波

陈小龙, 马磊, 张文旭

西南交通大学系统科学与技术研究所, 成都 610031

收稿日期:2015-01-26 修回日期:2015-03-24 出版日期:2015-07-10 发布日期:2015-07-17
作者简介:马磊(1972-),男,贵州贵阳人,教授,博士,主要研究方向:机器人控制、多机器人系统、新能源控制; 张文旭(1985-),男,甘肃兰州人,博士研究生,主要研究方向:多机器人系统、机器学习与决策。
基金资助:
国家自然科学基金资助项目(61075104)。

Quantized distributed Kalman filtering based on dynamic weighting

CHEN Xiaolong, MA Lei, ZHANG Wenxu

Institute of System Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China

Received:2015-01-26 Revised:2015-03-24 Online:2015-07-10 Published:2015-07-17
Contact: 陈小龙(1989-),男,广西玉林人,硕士研究生,主要研究方向:多机器人系统、分布式滤波算法,arthur_small@foxmail.com

摘要/Abstract

摘要： 针对一个无融合中心传感器网络中的状态估计问题,提出一种基于量化信息的分布式卡尔曼滤波(QDKF)算法。首先,在分布式卡尔曼滤波(DKF)中,以节点状态估计精度为加权准则,动态选取加权矩阵,使得全局估计误差的协方差最小;然后,进一步考虑了网络带宽受限制的情况,在DKF算法中加入均匀量化器,节点之间通信使用量化后的信息,以减少网络通信的带宽需求。QDKF算法仿真采用了8 bit的均匀量化器,与Metropolis加权法和最大度加权法相比,动态加权法的状态估计均方根误差分别降低了25%和27.33%。实验结果表明,采用动态加权法的QDKF算法能提高系统的状态估计精度,减少带宽需求,适用于网络通信受限制的应用场合。

关键词: 无线传感器网络, 分布式算法, 量化信息, 一致性滤波, 动态加权

Abstract: Focusing on the state estimation problem of a Wireless Sensor Network (WSN) without a fusion center, a Quantized Distributed Kalman Filtering (QDKF) algorithm was proposed. Firstly, based on the weighting criterion of node estimation accuracy, a weight matrix was dynamically chosen in the Distributed Kalman Filtering (DKF) algorithm to minimize the global estimation Error Covariance Matrix (ECM). And then, considering the bandwidth constraint of the network, a uniform quantizer was added into the DKF algorithm. The requirement of the network bandwidth was reduced by using the quantized information during the communication. Simulations were conducted by using the proposed QDKF algorithm with an 8-bit quantizer. In the comparison experiments with the Metropolis weighting and the maximum degree weighting, the estimation Root Mean Square Error (RMSE) of the mentioned dynamic weighting method decreased by 25% and 27.33% respectively. The simulation results show that the QDKF algorithm using dynamic weighting can improve the estimation accuracy and reduce the requirement of network bandwidth, and it is suitable for network communications limited applications.

Key words: Wireless Sensor Network (WSN), distributed algorithm, quantized information, consensus filtering, dynamic weighting

中图分类号:

陈小龙, 马磊, 张文旭. 基于动态加权的量化分布式卡尔曼滤波[J]. 计算机应用, 2015, 35(7): 1824-1828.

CHEN Xiaolong, MA Lei, ZHANG Wenxu. Quantized distributed Kalman filtering based on dynamic weighting[J]. Journal of Computer Applications, 2015, 35(7): 1824-1828.

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基于动态加权的量化分布式卡尔曼滤波

Quantized distributed Kalman filtering based on dynamic weighting

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