Yang Gongliu, Liu Yuanyuan, Li Ming, Song Shunguang
School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China.
Inertial Technology Key Laboratory of National Defense Science and Technology, Beihang University, Beijing 100191, China.
Sensors (Basel). 2015 Oct 23;15(10):26940-60. doi: 10.3390/s151026940.
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
提出了一种改进的双因子自适应卡尔曼滤波器，称为AMA-RWE-DFAKF，用于在静态和动态条件下对光纤陀螺（FOG）漂移信号进行去噪。第一个因子是通过对创新序列协方差矩阵的随机加权估计（RWE）随时更新卡尔曼增益，以确保输出的噪声水平最低，但在动态条件下KF响应的惯性会增加。为了降低惯性，第二个因子是仅在通过自适应移动平均（AMA）检测到不连续时才由RWE调整预测状态向量的协方差矩阵。将AMA-RWE-DFAKF应用于FOG静态和动态信号的去噪，并将其性能与传统卡尔曼滤波器（CKF）、基于随机加权估计且带有增益校正的自适应卡尔曼滤波器（RWE-AKFG）、基于自适应移动平均和随机加权估计的双模自适应卡尔曼滤波器（AMA-RWE-DMAKF）进行比较。静态信号的阿伦方差结果和动态信号的均方根误差（RMSE）结果表明，该算法在去噪FOG信号方面优于所有考虑的方法。
