An Adaptive Iterated Extended Kalman Filter for Target Tracking

被引：0
|
作者：
Yan, Chunman [1 ,2 ]
Dong, Junsong [1 ]
Lu, Genyuan [1 ]
Zhang, Daoliang [1 ]
Chen, Manli [1 ]
Cheng, Liang [1 ]
机构：
[1] Northwest Normal Univ, Coll Phys & Elect Engn, Lanzhou 730070, Peoples R China
[2] Engn Res Ctr Gansu Prov Intelligent Informat Tech, Lanzhou 730070, Peoples R China
来源：
INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING | 2018年 / 11266卷
基金：
中国国家自然科学基金;
关键词：
Target tracking; Iterative extended Kalman filter; Strong tracking filter; Innovation;
D O I：
10.1007/978-3-030-02698-1_32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
For the iterated extended Kalman filter (IEKF) in target tracking, the system model and noise estimation are always uncertain. In view of these problems, an improved adaptive iterated extended Kalman filter is proposed. The new algorithm is based on IEKF, combines the improved strong tracking filter to make it more fitting the maneuvering target tracking issue. Moreover, in our approach, the noise variance is adjusted in real-time by a noise parameter estimator which is based on the seasonable statistic characteristic of the noise. The estimator can availably reduce the influence of the time-varying noise. The simulation results indicate that the improved algorithm has higher estimation accuracy on the target position and speed for target tracking.
引用
收藏
页码：371 / 377
页数：7
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