UKF-based MEMS micromirror angle estimation for LiDAR

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.