电子测量与仪器学报

冯伟,吴英,邓义廷,康鹏川,路永乐,刘宇.基于大动态环境下的高阶迭代姿态优化算法[J].电子测量与仪器学报,2022,36(8):28-34

基于大动态环境下的高阶迭代姿态优化算法

High order iterative attitude optimization algorithm based onlarge dynamic environment

DOI：

英文关键词:inertial navigation system non-commutative error large dynamics higher order iteration attitude calculation

基金项目:国家自然科学基金(52195531,5217053290)、重庆市教委科学技术研究基金（KJZD M202000602）项目资助

作者	单位
冯伟	1.重庆邮电大学自主导航与微系统重庆市重点实验室
吴英	1.重庆邮电大学自主导航与微系统重庆市重点实验室
邓义廷	1.重庆邮电大学自主导航与微系统重庆市重点实验室
康鹏川	1.重庆邮电大学自主导航与微系统重庆市重点实验室
路永乐	1.重庆邮电大学自主导航与微系统重庆市重点实验室
刘宇	1.重庆邮电大学自主导航与微系统重庆市重点实验室

Author	Institution
Feng Wei	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications
Wu Ying	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications
Deng Yiting	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications
Kang Pengchuan	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications
Lu Yongle	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications
Liu Yu	1.Chongqing Key Laboratory of Autonomous Navigation and Microsystem, Chongqing University of Posts and Telecommunications

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中文摘要:

针对 MEMS 惯性导航系统大动态坏境下不可交换误差问题,提出了一种改进的高阶迭代姿态优化算法。为解决大动态环境下不可交换误差对整个惯性导航系统带来的影响,推导了传统等效旋转矢量算法,针对此算法仅依靠提高子样数来提高解算精度,忽略了高阶项在大动态环境下会产生较大误差的问题。设计了快慢回路的方法,分别求得不同阶次的旋转矢量解, 再通过周期性迭代算法得到快慢回路的迭代解。最后通过大动态环境仿真实验以及高精度三轴转台摇摆动态实验,验证了高阶迭代算法的性能优势。实验结果表明,大动态环境下,相较于传统算法,改进的高阶迭代姿态优化算法精度提高了两个数量级。

英文摘要:

Aiming at the problem of non-commutative errors in the large dynamic environment of MEMS inertial navigation system, an improved high-order iterative attitude optimization algorithm is proposed. In order to solve the influence of non-exchangeable errors on the entire inertial navigation system in a large dynamic environment, the traditional equivalent rotation vector algorithm is deduced. For this algorithm, it only relies on increasing the number of subsamples to improve the solution accuracy, ignoring the problem that high-order terms will cause large errors in a large dynamic environment. Using the method of fast and slow loops, the rotation vector solutions of different orders are obtained respectively, and then the iterative solutions of the fast and slow loops are obtained through the periodic iterative algorithm. Finally, through the large dynamic environment simulation experiment and the high-frequency swing dynamic experiment of the high-precision three-axis turntable, the performance advantage of the higher-order iterative algorithm is verified. The experimental results show that in a large dynamic environment, compared with the traditional algorithm, the improved high-order iterative attitude optimization algorithm improves the accuracy by two orders of magnitude.

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