中国舰船研究2024，Vol.19Issue(3)：344-352,9.DOI:10.19693/j.issn.1673-3185.03314
基于两个点集间参数估计的DVL标定方法
A method for DVL calibration based on parameter estimation between two point sets
摘要
Abstract
[Objective]In order to improve the combined navigation accuracy of unmanned surface vehicles(USVs)by strapdown inertial navigation system(SINS)and doppler range finder(DVL),the DVL error needs to be accurately calibrated.[Methods]This paper carries out error modeling of the installation error angle,lever arm,and scale factor of the DVL,and focuses on the analysis of the impact of the lever arm error term on the combined navigation positioning in the vessels,pointing out the necessity of its calibration;proposes a DVL error calibration method based on the parameter estimation between two point sets,which treats the SINS/GNSS navigation parameters and DVL output parameters as two point sets,converting the calibration prob-lem into an estimation problem of the difference between the two point sets,and using the Kalman filter to es-timate the error parameters between the two point sets;using the SVD(singular value decomposition)-based observability analysis to quantify the observability of the filter in different motion condtions,giving the mo-tion recommendations of the carrier during the calibration process.[Results]The results of mathematical simulation and real ship experiments show that the global relative accuracy of the calibrated SINS/DVL al-gorithm can reach a voyage of 0.122%.During cornering maneuvers,the lever arm causes abrupt changes in SINS/DVL positioning.After compensating for the lever arm,the positioning errors become smoother and the overall positioning error is reduced.[Conclusion]The proposed method provides a feasible way to calibrate each DVL error including the lever arm.关键词
无人艇/SINS/DVL组合导航/多普勒计程仪标定/杆臂误差/可观测性分析Key words
unmanned surface vehicles/SINS/DVL combined navigation/DVL calibration/lever arm er-ror/observability analysis分类
交通工程引用本文复制引用
罗皓,刘锡祥,刘剑威,吴小强,程相智..基于两个点集间参数估计的DVL标定方法[J].中国舰船研究,2024,19(3):344-352,9.基金项目
国家自然科学基金资助项目(51979041,61973079) （51979041,61973079）
