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 on large dynamic environment

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。

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.