基于最小均方和扩展卡尔曼滤波的IMU降噪方法

IMU De-Noising Method Based on Least Mean Square Algorithm and Extended Kalman Filter

摘要:

针对车体振动影响微机电系统惯性测量单元(MEMS IMU)航向精度问题,提出了一种能有效抑制振动噪声从而提升航向精度与稳定性的方法。首先,采用最小均方法对数据进行前端预处理,以提升信噪比；然后,利用加速度计与陀螺仪的互补特性滤除陀螺仪的零偏噪声；最后,采用扩展卡尔曼滤波进一步滤波。总计4h的现场实验结果表明:IMU受载体振动影响较小,航向的精度与稳定性得到提升；其中,在大角度机械运动后的相对航向误差为3.08°,静止时的航向方差为2.44×10-5。

Abstract:

Aiming at the problem that vehicle vibration affects the heading accuracy of MEMS IMU, a method that can effectively suppress vibration noise and improve heading accuracy and stability is proposed. Firstly, the minimum mean method was used to preprocess the data to improve the signal-to-noise ratio. Then, the bias noise of gyroscope was filtered by using the complementary characteristics of accelerometer and gyroscope. Finally, the extended Kalman filter was used for further filtering. A total of 4 hours of field experiment results show that IMU is less affected by carrier vibration, and the accuracy and stability of heading are improved. The relative heading error after large-angle mechanical motion is 3.08° and the heading variance at rest is 2.44×10-5.