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Sensors 2017, 17(1), 152; doi:10.3390/s17010152

Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2
University of Chinese Academy of Sciences, Beijing 10049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mustafa Yavuz
Received: 2 October 2016 / Revised: 19 December 2016 / Accepted: 31 December 2016 / Published: 14 January 2017
(This article belongs to the Special Issue MEMS and Nano-Sensors)
View Full-Text   |   Download PDF [1911 KB, uploaded 14 January 2017]   |  

Abstract

In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment. View Full-Text
Keywords: MEMS IMU; transfer alignment; adaptive incremental Kalman filter; information fusion MEMS IMU; transfer alignment; adaptive incremental Kalman filter; information fusion
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Chu, H.; Sun, T.; Zhang, B.; Zhang, H.; Chen, Y. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter. Sensors 2017, 17, 152.

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