Consensus on IMM-UKFs for Tracking in Mixed LOS/NLOS Conditions

被引：0

作者：

Xiang, Lidan ^{[1
]}

Zhou, Yan ^{[1
]}

机构：

[1] Xiangtan Univ, Coll Informat Engn, Xiangtan 411105, Peoples R China

来源：

PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年

基金：

中国国家自然科学基金;

关键词：

robot group; target tracking; average consensus; non-line-of-sight; interactive multiple model; unscented Kalman filter; MOBILE LOCATION ESTIMATOR;

D O I：

10.23919/chicc.2019.8865882

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The problem of distributed target tracking in mixed LOS/NLOS conditions is addressed using average consensus in this paper. Unlike the existing methods that send local measurements or estimates to a fusion center and perform centralized measurement/ track fusion, a totally distributed estimation fusion framework with only one-hop neighbors' information exchange is developed. First, based on local time of arrival (TOA) and received signal strength (RSS) measurements, each robot runs an interactive multiple model of unscented Kalman filter (IMM-UKF) to estimate the target state in parallel. Then to distribute the centralized track fusion algorithm, average consensus is used to estimate global information contribution through information interaction between neighbors. Simulation results show that for the proposed consensus tracking, each robot performs like a fusion center and has nearly identical accuracy to the optimal centralized fusion strategy, while the communication burden has been extensively reduced.

引用

页码：4427 / 4432

页数：6

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