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Attitude measurement of multi-sensor fusion based on IQPSO-EKF

Published:2024-03-06 author:HU Qiguo, WANG Lei, MA Jianwang, et al. Browse: 1267 Check PDF documents

Attitude measurement of multi-sensor fusion based on IQPSO-EKF

HU Qiguo, WANG Lei, MA Jianwang, REN Yurong

(School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract: To solve the influence of the positioning and attitude adjustment accuracy of automatic shaft boring equipment on the excavation efficiency and quality of shaft and hole pile, an attitude measurement algorithm based on improved quantum particle swarm optimization (IQPSO)-extended Kalman filter (EKF) was proposed to improve the measurement accuracy of micro-electro-mechanical system (MEMS) sensors. First, the MEMS sensor data was preprocessed (noise removal, filtering, calibration, etc.). Then, the attitude solving model was established with reference to the coordinate system of the existing aircraft, the EFK state equation was constructed through the attitude angle mathematical model and kinematic analysis, and the initial population was optimized by piecewise chaotic mapping for the problem of inaccurate estimation of EKF method parameters. The average position optimal value was introduced to avoid falling into the local optimal IQPSO-EFK algorithm, and the EKF system was optimized to measure the covariance parameters of noise. Finally, the comparative experiments were conducted on the improved algorithm and the three sets of attitude error estimation. The research results show that comparing with the three typical objective functions, IQPSO-EFK has stronger optimization ability and convergence accuracy than ordinary particle swarm operation (QPSO-EFK). Comparing with the three sets of rotational velocity attitude measurement errors, the attitude measurement method based on IQPSO-ECKF algorithm reduces the measurement error by about 86.3% compared with the real measurement error, about 68.7% compared to the extended Kalman filter, and about 28.2% compared to the ordinary particle swarm algorithm, which proves that the algorithm effectively improves the measurement accuracy of MEMS sensors.
Key words: shaft boring; angle measuring instrument; attitude measurement; micro-electro-mechanical system(MEMS) sensors; multisensor fusion; improved quantum particle swarm optimization-extended Kalman filter(IQPSO-EKF)

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