Data fusion of MEMS IMU/GPS integrated system for autonomous land vehicle

Meiling Wang

doi:10.1109/ICIA.2006.305846

Data fusion of MEMS IMU/GPS integrated system for autonomous land vehicle

Meiling Wang^*

^*此作品的通讯作者

自动化学院

科研成果: 会议稿件 › 论文 › 同行评审

4 引用（Scopus）

摘要

To improve the performance of MEMES IMU based integrated system, the data fusion method integrated AI and Kalman filter was discussed. First, GPS signal validity and vehicle motion status were identified by using fuzzy logics. And when GPS doesn't outage, its data were set higher weight to be used to guide ALV; otherwise, AI (Artificial Intelligence) based integration algorithm with the help of Kalman filter was adopted. The method can make full use of the advantages of KF (Kalman Filter) and AI, and reduce their limitations.

源语言	英语
页	80-84
页数	5
DOI	https://doi.org/10.1109/ICIA.2006.305846
出版状态	已出版 - 2006
活动	2006 IEEE International Conference on Information Acquisition, ICIA 2006 - Weihai, Shandong, 中国期限: 20 8月 2006 → 23 8月 2006

会议

会议	2006 IEEE International Conference on Information Acquisition, ICIA 2006
国家/地区	中国
市	Weihai, Shandong
时期	20/08/06 → 23/08/06

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10.1109/ICIA.2006.305846

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title = "Data fusion of MEMS IMU/GPS integrated system for autonomous land vehicle",

abstract = "To improve the performance of MEMES IMU based integrated system, the data fusion method integrated AI and Kalman filter was discussed. First, GPS signal validity and vehicle motion status were identified by using fuzzy logics. And when GPS doesn't outage, its data were set higher weight to be used to guide ALV; otherwise, AI (Artificial Intelligence) based integration algorithm with the help of Kalman filter was adopted. The method can make full use of the advantages of KF (Kalman Filter) and AI, and reduce their limitations.",

keywords = "ALV, Artificial intelligence, Dead-reckoning, Kalman filter, MEMS IMU/GPS",

author = "Meiling Wang",

year = "2006",

doi = "10.1109/ICIA.2006.305846",

language = "English",

pages = "80--84",

note = "2006 IEEE International Conference on Information Acquisition, ICIA 2006 ; Conference date: 20-08-2006 Through 23-08-2006",

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AU - Wang, Meiling

PY - 2006

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N2 - To improve the performance of MEMES IMU based integrated system, the data fusion method integrated AI and Kalman filter was discussed. First, GPS signal validity and vehicle motion status were identified by using fuzzy logics. And when GPS doesn't outage, its data were set higher weight to be used to guide ALV; otherwise, AI (Artificial Intelligence) based integration algorithm with the help of Kalman filter was adopted. The method can make full use of the advantages of KF (Kalman Filter) and AI, and reduce their limitations.

AB - To improve the performance of MEMES IMU based integrated system, the data fusion method integrated AI and Kalman filter was discussed. First, GPS signal validity and vehicle motion status were identified by using fuzzy logics. And when GPS doesn't outage, its data were set higher weight to be used to guide ALV; otherwise, AI (Artificial Intelligence) based integration algorithm with the help of Kalman filter was adopted. The method can make full use of the advantages of KF (Kalman Filter) and AI, and reduce their limitations.

KW - ALV

KW - Artificial intelligence

KW - Dead-reckoning

KW - Kalman filter

KW - MEMS IMU/GPS

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U2 - 10.1109/ICIA.2006.305846

DO - 10.1109/ICIA.2006.305846

M3 - Paper

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EP - 84

T2 - 2006 IEEE International Conference on Information Acquisition, ICIA 2006

Y2 - 20 August 2006 through 23 August 2006

ER -

Data fusion of MEMS IMU/GPS integrated system for autonomous land vehicle

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