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^*

^*Corresponding author for this work

School of Automation

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages	80-84
Number of pages	5
DOIs	https://doi.org/10.1109/ICIA.2006.305846
Publication status	Published - 2006
Event	2006 IEEE International Conference on Information Acquisition, ICIA 2006 - Weihai, Shandong, China Duration: 20 Aug 2006 → 23 Aug 2006

Conference

Conference	2006 IEEE International Conference on Information Acquisition, ICIA 2006
Country/Territory	China
City	Weihai, Shandong
Period	20/08/06 → 23/08/06

Keywords

ALV
Artificial intelligence
Dead-reckoning
Kalman filter
MEMS IMU/GPS

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10.1109/ICIA.2006.305846

Cite this

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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",

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

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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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ER -

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

Abstract

Conference

Keywords

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