北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (6): 1229-1236.doi: 10.13700/j.bh.1001-5965.2019.0348

• 论文 • 上一篇    

基于线性编码补偿方法的非固定丢包率下的分布式融合估计器

韩旭1, 赵国荣2, 王康2   

  1. 1. 中国人民解放军 91001部队, 北京 100036;
    2. 海军航空大学 岸防兵学院, 烟台 264001
  • 收稿日期:2019-07-01 发布日期:2020-07-02
  • 通讯作者: 赵国荣 E-mail:grzhao6881@163.com
  • 作者简介:韩旭 男,博士,助理研究员。主要研究方向:飞行器制导与控制、飞行器任务规划;赵国荣 男,博士,教授。主要研究方向:飞行器控制与导航技术;王康 男,博士,助理研究员。主要研究方向:飞行器故障检测。
  • 基金资助:
    国家自然科学基金(61473306)

A decentralized fusion estimator using linear coding compensation method with non-fixed dropout rates

HAN Xu1, ZHAO Guorong2, WANG Kang2   

  1. 1. Unit 91001 of the PLA, Beijing 100036, China;
    2. Coast Guard College, Naval Aviation University, Yantai 264001, China
  • Received:2019-07-01 Published:2020-07-02
  • Supported by:
    National Natural Science Foundation of China (11972192); the Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要: 为解决无线信道非固定丢包率建模和丢包补偿问题,研究了具有非固定丢包率的网络化多传感器融合估计问题。假定无线信道丢包率是非固定的,利用对过去得到的有限个测量值进行线性编码的方法对丢包进行补偿,针对系统矩阵中存在的非高斯非白噪声随机干扰,首先设计了一种利用每一时刻数据包到达变量的局部最优估计器,其次推导出融合估计误差协方差与传感器传输概率之间的函数关系。最后通过算例仿真验证所提方法的有效性。

关键词: 线性编码, 非高斯噪声, 非固定丢包率, 丢包补偿, 分布式融合估计

Abstract: The networked multi-sensor fusion estimation problem is investigated in the paper for a class of networked system with non-fixed packet loss rates,which aims to solving the problem of modeling of non-fixed packet loss rates in wireless channel and the problem of dropout compensation. The dropout rate of the wireless channel is assumed to be non-fixed. A linear coding method is used at the sensor by combining the past several measurements to get a new measurement, which compensates the data. First,a recursive locally optimal estimator is designed by minimizing the mean square error accounting for the non-Gaussian non-white noise random disturbance of the system matrix and making good use of the real-time arrival information based on the received measurement. Second,the functional relationship between the fusion estimation error covariance and sensor transmitting probability is derived. Finally, simulation example is given to confirm the effectiveness of the proposed method.

Key words: linear coding, non-Gaussian noise, non-fixed dropout rate, dropout compensation, decentralized fusion estimation

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