异质双9轴MEMS惯性传感器数据互补-加权迭代融合算法

doi:10.3969/j.issn.0255-8297.2015.05.004

Abstract

Abstract: In micro-electro-mechanical system (MEMS) inertial sensor navigation, traditional techniques only use single multi-axis sensor to navigate and get position of the moving target. However, measurements of sensors and noise characteristics are affected by environment conditions. Besides, acceleration error, gyro drift, platform angle error and the error of platform installation also have important influences. From the view point of data fusion of complementary non-single multi-axis sensor, this paper uses two 9-axis MEMS inertial sensors to collect the original information of moving targets, and develops a complementary-weighted iterative fusion algorithm. The raw data of heterogeneous dual 9-axis MEMS inertial sensor are preprocessed. Then, using minimum variance, the observed values are estimated with Kalman filtering. A model of weight updating is established to improve accuracy according to evaluation of variance and an innovation equation. Experimental results show that, comparing with the traditional method, the proposed algorithm can improve navigation accuracy by more than 50%.

Key words: navigation, micro-electro-mechanical system (MEMS), data fusion, Kalman filtering

CLC Number:

TP273

WANG Wei, ZHANG Jin-yi, ZHANG Hong-hui, CAI Chun-yan, LI Jian-yu. Complementary-Weighted Iterative Fusion Algorithm for Heterogeneous Dual 9-Axis MEMS Inertial Sensor Data[J]. Journal of Applied Sciences, 2015, 33(5): 491-501.

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Complementary-Weighted Iterative Fusion Algorithm for Heterogeneous Dual 9-Axis MEMS Inertial Sensor Data

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