Fuzzy Adaptive Kalman filtering for DR/GPS

被引：15

作者：

Zhang, ST ^{[1
]}

Wei, XY ^{[1
]}

机构：

[1] No Jiaotong Univ, Coll Elect & Informat Engn, Beijing 100044, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

sensor fusion; fuzzy logic control; Adaptive Kalman filtering; navigation;

D O I：

10.1109/ICMLC.2003.1259976

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, a novel method of multi-sensor data fusion based on the Adaptive Fuzzy Kalman Filter is presented. This method is applied in fusing position and orientation (or direction) signals from Dead Reckoning (DR) system and the Global Positioning System (GPS) for landing vehicle navigation. The Extended Kalman Filter (EKF) and the characteristics of the measurement noise are modified by using the Fuzzy Adaptive system, and Fuzzy Adaptive system is based on a covariance matching technique. It is compared with the performance of a regular EKF. It is demonstrated that Fuzzy Adaptive Kalman Filter is better (more accurate) than the EKF, and the algorithm is not complex. It is important to improve the accuracy of the vehicle navigation system.

引用

页码：2634 / 2637

页数：4

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