TY - GEN
T1 - A Novel Temperature Error Compensation method for MEMS Gyros Based on WOA-SVR
AU - Yang, Gongliu
AU - Zhang, Kun
AU - Cheng, Ruizhao
AU - Zhang, Yongfeng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/20
Y1 - 2021/8/20
N2 - Most MEMS inertial navigation systems (INS) need to meet a wide working temperature range of - 20 ∼ + 55°C. MEMS gyroscope is the core component of MEMS INS and the accuracy of MEMS gyros directly affects the navigation performance. However, the output of MEMS gyros is inevitably affected by temperature. Due to the limited temperature error compensation accuracy of traditional method based on least squares polynomial fitting, the paper presents a new temperature error compensation method based on Whale Optimization Algorithm (WOA) optimized support vector regression (SVR), which is achieved the optimization of SVR by WOA. After simulation and MEMS gyros temperature error compensation test. The results show that the WOA-SVR can effectively compensate the temperature error of MEMS gyros. And the accuracy is significantly improved compared with the traditional methods.
AB - Most MEMS inertial navigation systems (INS) need to meet a wide working temperature range of - 20 ∼ + 55°C. MEMS gyroscope is the core component of MEMS INS and the accuracy of MEMS gyros directly affects the navigation performance. However, the output of MEMS gyros is inevitably affected by temperature. Due to the limited temperature error compensation accuracy of traditional method based on least squares polynomial fitting, the paper presents a new temperature error compensation method based on Whale Optimization Algorithm (WOA) optimized support vector regression (SVR), which is achieved the optimization of SVR by WOA. After simulation and MEMS gyros temperature error compensation test. The results show that the WOA-SVR can effectively compensate the temperature error of MEMS gyros. And the accuracy is significantly improved compared with the traditional methods.
KW - Micromechanical gyros
KW - Whale Optimization Algorithm
KW - support vector regression
KW - temperature compensation
UR - https://www.scopus.com/pages/publications/85117803067
U2 - 10.1109/CSAIEE54046.2021.9543296
DO - 10.1109/CSAIEE54046.2021.9543296
M3 - 会议稿件
AN - SCOPUS:85117803067
T3 - 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021
SP - 292
EP - 295
BT - 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021
Y2 - 20 August 2021 through 22 August 2021
ER -