北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (8): 1485-1493.doi: 10.13700/j.bh.1001-5965.2019.0497

• 论文 • 上一篇    下一篇

数据丢包和量化约束下的分布式滚动时域估计

刘帅1, 赵国荣1, 曾宾2, 高超1   

  1. 1. 海军航空大学 岸防兵学院, 烟台 264001;
    2. 中国人民解放军 92095部队, 台州 318000
  • 收稿日期:2019-09-11 发布日期:2020-08-27
  • 通讯作者: 赵国荣 E-mail:GRZhao6881@163.com
  • 作者简介:刘帅 男,博士研究生。主要研究方向:飞行器综合导航。
    赵国荣 男,博士,教授,博士生导师。主要研究方向:飞行器导航与控制。
  • 基金资助:
    国家自然科学基金(61701519,61903374)

Distributed moving horizon estimation under constraints of quantized measurements and packet dropouts

LIU Shuai1, ZHAO Guorong1, ZENG Bin2, GAO Chao1   

  1. 1. Coastal Defence Academy, Naval Aviation University, Yantai 264001, China;
    2. The Chinese People's Liberation Army 92095 Troop, Taizhou 318000, China
  • Received:2019-09-11 Published:2020-08-27
  • Supported by:
    National Natural Science Foundation of China (61701519,61903374)

摘要: 针对网络约束问题,对带丢包和量化的网络化系统分布式状态估计进行了研究。用一组满足Bernouli分布的随机变量来描述丢包现象,采用预测补偿机制进行丢包补偿。将数据量化引入的误差描述为观测方程中的参数不确定性,通过求解固定时域内的min-max问题,得到局部估计器。对局部估计器的稳定性进行研究,给出了误差范数平方的期望收敛的充分条件。推导了局部估计器误差协方差上界的递推公式,在此基础上,应用协方差交叉(CI)融合算法得到了分布式融合估计器。仿真结果表明,所提算法能够有效减小数据丢包和量化对状态估计的影响。

关键词: 分布式滚动时域估计, 预测补偿, 数据量化, 稳定性分析, 协方差交叉(CI)融合

Abstract: Aimed at the problem of network constraint, distributed state estimation for networked systems with packet dropouts and quantized measurements is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and a prediction compensation mechanism is applied to compensate the packet dropouts. Quantized errors introduced by data quantification are described as parameter uncertainty in the observation equation, and the local estimator is obtained by solving a min-max problem in fixed time domain. The stability of the local estimator is studied, and a sufficient condition for the convergence of the expectation of the square norm of estimation error is obtained. For each local estimator, the recursive formula of the upper bound of the error covariance is derived, based on which a distributed fusion estimator is presented by using the Covariance Intersection (CI) fusion algorithm. The simulation results show that the proposed algorithm can effectively reduce the influence of packet dropouts and quantization on state estimation.

Key words: distributed moving horizon estimation, prediction compensation, data quantification, stability analysis, Covariance Intersection (CI) fusion

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发