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首页> 外文期刊>Journal of Micromechanics and Microengineering >UKF-based MEMS micromirror angle estimation for LiDAR
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UKF-based MEMS micromirror angle estimation for LiDAR

机译：基于UKF的MEMS LIDAR的MEMS微镜角估计

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摘要

Azimuth and distance measurement are two key technologies of MEMS LIDAR. In order to improve the accuracy of (micro-electronical mechanical system scanning mirror) MEMS-SM angle measurement, this paper proposes an angle estimation algorithm based on unscented Kalman filter (UKF), which can reduce the sensor noise by using the motion model of MEMS-SM. First, the angle measurement is given by the built-in angle sensor or transfer function model of MEMS-SM. Secondly, the dynamic model is established according to the Lissajous scanning mode of MEMS-SM. Then the UKF algorithm can be presented, including the measurement equation and the state equation, where the nonlinear equation is the inverse trigonometric function. Finally, Laser Doppler Velocimeter was adopted as a standard instrument to verify the accuracy of the proposed algorithm. The results showed that the UKF angle estimation algorithm based on MEMS-SM dynamic model improved the accuracy of the built-in sensor's angle measurement by 5-10 times. And this method is suitable for LIDAR of different scanners' types and different scanning modes, which can meet the demand of imaging MEMS LIDAR for the accuracy and stability of angle measurement.
机译：方位角和距离测量是MEMS LIDAR的两个关键技术。为了提高（微电器机械系统扫描镜）MEMS-SM角度测量的精度，提出了一种基于Unscented Kalman滤波器（UKF）的角度估计算法，其可以通过使用运动模型来降低传感器噪声MEMS-SM。首先，角度测量由内置角度传感器或MEMS-SM的传递函数模型给出。其次，根据MEMS-SM的LISSAJOUS扫描模式建立动态模型。然后可以呈现UKF算法，包括测量方程和状态方程，其中非线性方程是逆三角函数。最后，采用激光多普勒速度计是标准仪器，以验证所提出的算法的准确性。结果表明，基于MEMS-SM动态模型的UKF角估计算法提高了内置传感器角度测量的精度5-10次。该方法适用于不同扫描仪类型和不同扫描模式的LIDAR，其可以满足成像MEMS LIDAR的需求，以获得角度测量的精度和稳定性。

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