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

页码：273 / 281

页数：9

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