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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter
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An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

机译：强跟踪卡尔曼滤波器的微机电系统陀螺仪温度漂移自适应补偿算法

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摘要

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
机译：我们提出了一种与微机电系统（MEMS）陀螺仪和指南针集成的系统的自适应算法，可消除环境影响，精确补偿温度漂移并提高MEMS陀螺仪的精度。我们使用简化的漂移模型并通过更改但适当的模型参数来实现此算法。 MEMS陀螺仪温度漂移模型主要是基于陀螺仪的温度灵敏度构建的。作为强跟踪卡尔曼滤波器（STKF）的状态变量，可以在指南针的支持下计算温度漂移模型的参数以适应环境。这些参数会随着环境的变化而智能改变，以在变化的温度下保持MEMS陀螺仪的精度。在静态温度实验中航向误差小于0.6°，在动态室外实验中航向误差保持在5°至-2°的范围内。这表明所提出的算法对变化的温度具有很强的适应性，并且在补偿陀螺仪的温度漂移并消除温度变化的影响方面，其性能明显优于KF和MLR。

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