A New Diffusion Kalman Algorithm Dealing with Missing Data

被引：1
|
作者：
Xiao, Shuangyi [1 ]
Mu, Nankun [1 ]
Chen, Feng [1 ]
机构：
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II | 2019年 / 11555卷
关键词：
Diffusion estimation; Kalman filtering; Missing data;
D O I：
10.1007/978-3-030-22808-8_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a novel modified distributed Kalman algorithm, which is a diffusion strategy that the state estimation is more precise while the system model is time-varying. Our focus is on the missing data gathered by a set of sensor nodes that may obtain incomplete information because of the harsh environment. Simulation results evaluate the performance of the proposed distributed Kalman filtering algorithm.
引用
收藏
页码：273 / 281
页数：9
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