Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique

被引：55
作者：
Kobayashi, K [1 ]
Cheok, KC [1 ]
Watanabe, K [1 ]
Munekata, F [1 ]
机构：
[1] Oakland Univ, Sch Engn & Comp Sci, Dept Elect & Syst Engn, Rochester, MI 48309 USA
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 1998年 / 45卷 / 03期
关键词：
differential global positioning system; fuzzy logic; Kalman filter; sensor fusion;
D O I：
10.1109/41.679010
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 ability to determine an accurate global position of a vehicle has many useful commercial and military applications. The differential global positioning system (DGPS) is one of the practical navigation tools used for this purpose. However, the DGPS has limitations arising from slow updates, signal interference, and limited accuracy. This paper describes how vehicle rate sensors can be used to help a DGPS overcome these limitations. The theoretical background for the sensor fusion is based on the principle of Kalman filtering and a fuzzy logic scheme. Validity of the method was verified by using experimental data from an actual automobile navigating around an urban area, The results demonstrated that the path of the automobile can be continuously traced with high accuracy and repeatability, in spite of the limitations of the DGPS.
引用
收藏
页码：510 / 518
页数：9
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[1]   SENSOR FUSION USING FUZZY LOGIC ENHANCED KALMAN FILTER FOR AUTONOMOUS VEHICLE GUIDANCE IN CITRUS GROVES [J].
Subramanian, V. ;
Burks, T. F. ;
Dixon, W. E. .
TRANSACTIONS OF THE ASABE, 2009, 52 (05) :1411-1422
[2]   Sensor fusion based on fuzzy Kalman filter [J].
Sasiadek, JZ ;
Khe, J .
ROMOCO'01: PROCEEDINGS OF THE SECOND INTERNATIONAL WORKSHOP ON ROBOT MOTION AND CONTROL, 2001, :275-283
[3]   Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter [J].
Jetto, L ;
Longhi, S ;
Vitali, D .
INTELLIGENT AUTONOMOUS VECHICLES 1998 (IAV'98), 1998, :213-218
[4]   Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter [J].
Jetto, L ;
Longhi, S ;
Vitali, D .
CONTROL ENGINEERING PRACTICE, 1999, 7 (06) :763-771
[5]   Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system [J].
Loebis, D ;
Sutton, R ;
Chudley, J ;
Naeem, W .
CONTROL ENGINEERING PRACTICE, 2004, 12 (12) :1531-1539
[6]   Fuzzy state noise-driven Kalman filter for sensor fusion [J].
Chauhan, S. ;
Patil, C. ;
Sinha, M. ;
Halder, A. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2009, 223 (G8) :1091-1097
[7]   Adaptive tuning of a Kalman filter using Fuzzy logic for attitude reference system [J].
Kim, Taerim ;
Do, Joocheol ;
Jung, Eunkook ;
Baek, Gyeongdong ;
Kim, Sungshin .
PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11), 2011, :310-313
[8]   The adaptive Kalman filter based on fuzzy logic for inertial motion capture system [J].
Jin, Mei ;
Zhao, Jinge ;
Jin, Ju ;
Yu, Guohui ;
Li, Wenchao .
MEASUREMENT, 2014, 49 :196-204
[9]   A modified kalman filtering via fuzzy logic system for ARVs location [J].
Jin, Wenrui ;
Zhan, Xingqun .
2007 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS I-V, CONFERENCE PROCEEDINGS, 2007, :711-716
[10]   Extended Kalman Filter based Fusion of Reliable Sensors using Fuzzy Logic [J].
Das, Tanmoy Kumar ;
Harischandra, P. A. Diluka ;
Abeykoon, A. M. Harsha S. .
2017 3RD INTERNATIONAL MORATUWA ENGINEERING RESEARCH CONFERENCE (MERCON), 2017, :58-63
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