| 摘要: |
| MEMS陀螺随机漂移误差是制约惯性导航精度的关键因素。本文针对标准kalman滤波器陀螺漂移处理方法中，随机动态系统的结构参数和噪声统计特性参数不准确的问题，采用自适应SHAKF(Sage-Husa Adaptive Kalman Filter)滤波器进行参数实时估计，提高陀螺漂移精度。基于此思想，建立了ARMA随机误差模型，搭建了MEMS陀螺组件实验系统，通过高精度三轴转台静态测试采集陀螺数据。Aallan方差分析表明，零偏不稳定性经线性KF滤波后提升17.4%，经自适应SHAKF滤波后提升26.2%。 |
| 关键词: ARMA MEMS陀螺 随机漂移 SHAKF Kalman滤波 |
| DOI： |
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| 基金项目:煤矿防爆无人驾驶车辆 |
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| Stochastic error modeling compensation of MEMS gyroscope based on UKF filter |
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| Abstract: |
| The random drift error of MEMS gyroscope is the key factor to the precision of inertial navigation. According to the standard of gyro drift of Kalman filter processing method, statistical characteristics of structural parameters and noise parameters of random dynamic system precision problem, using adaptive SHAKF (Sage-Husa Adaptive Kalman Filter) filter for real-time estimation of parameters, improve the accuracy of gyro drift. Based on this idea, the ARMA random error model is established, and the experimental system of MEMS gyro component is built, and the gyro data is collected by the high precision three axis turntable. Aallan variance analysis shows that the zero bias instability is increased by 17.4% after the linear KF filter, and the adaptive SHAKF filter is improved by up to 26.2%. |
| Key words: ARMA MEMS gyroscope Random drift SHAKF Kalman filter |