A calibration method of ultra-short baseline installation error with large misalignment based on variational Bayesian unscented Kalman filter.
Liang Zhang, Tao Zhang, Jin-Wu Tong, Cheng-Cheng Weng, Yao Li
Author Information
Liang Zhang: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China. ORCID
Tao Zhang: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China. ORCID
Jin-Wu Tong: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China. ORCID
Cheng-Cheng Weng: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China.
Yao Li: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China.
The ultrashort baseline system is widely used in ships and underwater navigation and positioning. The misalignment and level arm with the inertial measurement unit are the two sources of positioning inaccuracy. The accuracy of calibration is usually affected by measurement noise and linearization of the observation equation. In order to improve the calibration accuracy, the variational Bayesian unscented Kalman filter (VBUKF) method is proposed for the calibration of the ultrashort baseline installation error in the paper. The detailed derivation of VBUKF for the calibration is presented in the paper. Simulation experiments and field experiments were carried out, respectively, to verify the algorithm. The simulation results show that the proposed method can calibrate the installation error of the sensors in real time on line. The field experiment verified that the algorithm improves the calibration accuracy of the installation error under the large misalignment. The positioning accuracy is also improved compared with the traditional method.
