Iterative Unbiased Converted Measurement Kalman Filter for Target Tracking

被引：3
|
作者：
Li, Da [1 ,2 ]
Zou, Xiangyu [1 ,2 ]
Li, Ruifang [1 ,2 ]
机构：
[1] Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China
[2] Wuhan Univ Technol, Key Lab Fiber Opt Sensing Technol & Informat Proc, Minist Educ, Wuhan 430070, Hubei, Peoples R China
来源：
2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL. 1 | 2017年
基金：
中国国家自然科学基金;
关键词：
iteration method; Kalman filter; target tracking; unbiased converted measurement;
D O I：
10.1109/ISCID.2017.52
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is important to develop a robust and fast tracking algorithm for modern tracking system because the target moves more and more fast. Therefore, a novel algorithm named the iterative unbiased converted measurement Kalman filter (IUCMKF) is proposed based on the analysis and comparison of conventional target tracking methods. The new algorithm is developed from unbiased converted measurement Kalman filter, but it can obtain more accurate state and covariance estimation. Compared with the existing target tracking approaches, the proposed method has potential advantages in tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation results.
引用
收藏
页码：342 / 345
页数：4
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