基于自适应遗传算法的MEMS加速度计 快速标定方法

微惯性测量单元（MIMU）的标定技术是低精度惯性导航领域中的重要研究方向，传统标定方法操作复杂，标定精度严重依赖转台精度。为解决大批量MIMU快速标定的问题，提出了一种基于自适应遗传算法（GA）的微机电系统（MEMS）加速度计快速标定方法，将加速度计标定问题转化为参数优化问题。首先，利用模观测原理构造目标优化函数；然后，分析系统可观测度确定最优标定编排方案；最后，采用全局搜索的自适应遗传算法优化标定参数。实验结果表明:与牛顿迭代法相比，标定精度提升1~3个数量级，运算速度提高61%。标定后解算的水平姿态角误差小于0.1°，可实现与传统标定方法相同量级的姿态精度，验证了所提方法的优越性和实用性。 

Rapid calibration method of MEMS accelerometer based on adaptive GA

MEMS inertial measurement unit (MIMU) calibration is one of an important research direction in low-precision inertial navigation. Traditional calibration method has complex operating procedures and depends most on turntable accuracy. In order to overcome the problem of MIMU calibration in batch production, this paper presents a rapid micro-electro-mechanical system (MEMS) accelerometer calibration method based on adaptive genetic algorithm (GA), which converts calibration task to parameter optimization. Firstly, the principle of norm observation is adopted to establish the objective optimization function. Secondly, the optimal calibration scheme is designed on the basis of system observability analysis. Finally, calibration parameters can be optimized through adaptive GA with global search capability. Experimental results demonstrate that, compared with Newton's iteration, the proposed method can improve calibration accuracy by 1-3 orders of magnitude and increase operational speed by 61%. After the proposed calibration, the horizontal attitude error is less than 0.1° and its accuracy can reach the same order of magnitude as that in traditional method, which verifies its superiority and practicability.