Distributed Adaptive Two-stage Kalman Filter for Target Tracking in the Presence of Unknown Dynamic Bias

被引：0
|
作者：
Zhang Cui [1 ,2 ]
Jia Yingmin [1 ,2 ]
Du Junping [3 ]
Zhang Jun [4 ]
机构：
[1] Beihang Univ BUAA, Res Div 7, Beijing 100191, Peoples R China
[2] Beihang Univ BUAA, Dept Syst & Control, Beijing 100191, Peoples R China
[3] Beijing Univ Posts & Telecommun, Sch Comp Sci & Technol, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
[4] Beihang Univ BUAA, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
来源：
2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年
关键词：
Dynamic Bias; Adaptive Two-stage Kalman Filter; Distributed Fusion; Multi-sensor System;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with the problem of tracking target with multiple sensors in the presence of unknown dynamic bias. A suboptimal adaptive two-stage Kalman filter (ATKF) is designed with two reduce-order filters to estimate the target state and the dynamic bias in parallel when the bias model information is incomplete. Moreover, a distributed adaptive two-stage Kalman filter (DATKF) is developed for multi-sensor system based on the ATKF. The effectiveness of the ATKF and the DATKF are illustrated by the Monte Carlo simulation results.
引用
收藏
页码：7216 / 7221
页数：6
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