基于变结构ESKF的航姿参考系统噪声处理方法

针对航姿参考系统（AHRS）易受到环境与传感器自身噪声干扰，导致姿态估计精度下降的问题，提出了一种基于变结构误差状态卡尔曼滤波（VS-ESKF）的噪声数据处理方法。首先，通过分析AHRS传感器观测数据与新息序列统计特征，设计了基于加速度范数与遗忘序贯概率比检验（F-SPRT）的方法，分别检测加速度计与陀螺仪的噪声数据。其次，基于噪声检测结果，将平滑变结构滤波(SVSF）策略引入到误差状态卡尔曼滤波（ESKF），以提高ESKF对噪声模型不确定性的处理能力。然后，结合磁场强度与磁倾角参数特征，利用马氏距离法评估磁干扰并实时调整磁力计补偿权重，获取准确的AHRS修正数据。最后，基于自主搭建独轮机器人平台进行实验验证，结果表明所设计的VS-ESKF算法可以及时、准确地检测AHRS噪声数据，并有效地抑制噪声干扰，相比于ESKF算法，对横滚角、俯仰角和偏航角的估计精度分别提升了31.05%、32.32%和40.07%，提高了姿态估计的准确性和稳定性。

AHRS noise processing method based on variable structure ESKF

To address the issue of decreased attitude estimation accuracy caused by environmental interference and sensor noise affecting the attitude and heading reference system (AHRS), a noise data processing method based on variable structure error state Kalman filtering (VS-ESKF) is proposed. The text describes a method for detecting noise data in accelerometers and gyroscopes by analyzing the statistical characteristics of sensor observation data and innovation sequence in AHRS. The method is based on the acceleration norm and forgotten sequential probability ratio test (F-SPRT). Secondly, the smooth variable structure filtering (SVSF) strategy is introduced into the error state Kalman filtering (ESKF) to improve its processing capability on the uncertainty of the noise model, based on the noise detection results. The magnetic disturbances are evaluated and magnetometer compensation weights are adjusted in real-time using the Mahalanobis distance method to obtain accurate AHRS correction data by combining the magnetic field strength and magnetic inclination parameter characteristics. The designed VS-ESKF algorithm can detect AHRS noise data timely and accurately, and effectively suppress noise interference, as demonstrated by experimental validation based on a self-unicycle robot platform. Compared to the ESKF algorithm, the accuracy of estimating the roll angle, pitch angle, and yaw angle has increased by 31.05%, 32.32%, and 40.07%, respectively. This improvement enhances the accuracy and stability of attitude estimation.