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An improved adaptive Kalman filter for denoising fiber optic gyro drift signal

机译:用于去噪光纤陀螺漂移信号的改进型自适应卡尔曼滤波器

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In this paper, an innovation based adaptive estimation Kalman filter (IAE-AKF) with double transitive factors is proposed for denoising the fiber optic gyroscope (FOG) signal. In this algorithm, double transitive adaptive factors are described in two stages. The transitive factor is introduced into the predicted state vector equation in stage one, where as in second stage, adaptive factor is scaled with measurement noise covariance matrix (R). These adaptive factors are developed based on the innovation sequence in adaptive Kalman filter. The predicted state error and measurement noise covariance matrix are updated by the double transitive adaptive factor in the process of iteration in stage one and two respectively. This algorithms is applied for denoising FOG signal in both static and dynamic conditions. The performance of proposed algorithm is compared with Conventional Kalman filter (CKF) and AKF with transitive factor. The precision improvement of FOG is calculated by variance and standard deviation, the predicted results revealed that the proposed algorithm is an efficient algorithm in drift denoising of FOG signal. In dynamic condition, the mean squared error (MSE) and root MSE (RMSE) values are calculated before and after denoising of FOG signal using proposed algorithm.
机译:本文提出了一种基于创新的具有双传递因子的自适应估计卡尔曼滤波器(IAE-AKF),用于对光纤陀螺仪(FOG)信号进行消噪。在该算法中,两个阶段都描述了双传递自适应因子。在第一阶段,将传递因子引入预测状态矢量方程,其中,与第二阶段一样,自适应因子通过测量噪声协方差矩阵(R)进行缩放。这些自适应因子是基于自适应卡尔曼滤波器中的创新序列而开发的。在第一阶段和第二阶段的迭代过程中,通过双重传递自适应因子更新预测状态误差和测量噪声协方差矩阵。该算法适用于在静态和动态条件下对FOG信号进行去噪。将该算法的性能与传统的卡尔曼滤波器(CKF)和具有传递因子的AKF进行了比较。通过方差和标准差计算出FOG的精度,预测结果表明,该算法是一种有效的FOG信号漂移去噪算法。在动态条件下,使用提出的算法在对FOG信号进行去噪之前和之后,计算均方误差(MSE)和均方根误差(RMSE)值。

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