基于Allan方差和SVR的MEMS陀螺仪随机误差分析与预测

为了使低成本MEMS陀螺仪数据的精度更高，本文提出了一种混合核函数支持向量回归（SVR）的MEMS陀螺仪随机误差预测模型，并通过粒子群优化（PSO）算法对模型参数和核函数参数进行优化；同时通过Allan方差法对SVR预测前后的MEMS陀螺仪随机误差数据进行分析。试验结果表明：混合核函数SVR对MEMS陀螺仪随机误差的预测准确度可达99.99%；当MEMS陀螺仪所处状态不同，但噪声特性相同时，可采用统一的SVR预测模型预测随机误差，研究结果为进一步用于MEMS陀螺仪的实时误差补偿中提供依据。

Random error analysis and prediction of MEMS gyroscope based on allan variance and SVR

In order to make the precision of low-cost MEMS gyroscope data higher, this paper proposes a hybrid kernel function support vector regression (SVR) MEMS gyroscope random error prediction model, and through the particle swarm optimization (PSO) algorithm for model parameters and kernel. The function parameters are optimized. At the same time, the MEMS gyro random error data before and after prediction is analyzed by the Allan variance method. The experimental results show that the hybrid kernel function SVR can predict the random error of MEMS gyroscope up to 99.99%. When the MEMS gyroscope is in different state but the noise characteristics are the same, a unified SVR prediction model can be used to predict the random error. The results provide a basis for further real-time error compensation for MEMS gyroscopes.