Song Ningfang, Yuan Zhengguo, Pan Xiong
Appl Opt. 2019 Dec 10;58(35):9505-9513. doi: 10.1364/AO.58.009505.
Reducing and suppressing the random noise and drift error is a critical task in an interferometric fiber-optic gyroscope (IFOG). In this paper, an improved adaptive Kalman filter (KF) based on innovation and random-weighting estimation (RWE) is proposed to denoise IFOG signals in both static and dynamic conditions. The covariance matrix of the innovation sequence is estimated using the random-weighted-average window. The KF gain is then adaptively updated by the estimated covariance matrix. To decrease the inertia of KF response in the dynamic condition, the covariance matrix of process noise is adjusted when discontinuous IFOG signals are detected by the innovation-based chi-square test method. The proposed algorithm is applied for denoising IFOG static and dynamic signals. Allan variance is used to evaluate the denoise performance for static signals. In the dynamic condition, root-mean-square error is considered as the performance indicator. Quantitative results reveal that the proposed algorithm is competitive for denoising IFOG signals when compared with conventional KF, RWE-based gain-adjusted adaptive KF, and RWE-based moving average double-factor adaptive KF.
降低和抑制随机噪声与漂移误差是干涉式光纤陀螺仪（IFOG）中的一项关键任务。本文提出了一种基于新息和随机加权估计（RWE）的改进自适应卡尔曼滤波器（KF），用于在静态和动态条件下对IFOG信号进行去噪。利用随机加权平均窗口估计新息序列的协方差矩阵。然后，通过估计的协方差矩阵自适应更新KF增益。为了降低动态条件下KF响应的惯性，当基于新息的卡方检验方法检测到不连续的IFOG信号时，调整过程噪声的协方差矩阵。将所提算法应用于IFOG静态和动态信号的去噪。使用阿伦方差评估静态信号的去噪性能。在动态条件下，将均方根误差作为性能指标。定量结果表明，与传统KF、基于RWE的增益调整自适应KF以及基于RWE的移动平均双因子自适应KF相比，所提算法在IFOG信号去噪方面具有竞争力。
