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为了降低光纤陀螺(FOG)的随机噪声以及消除异常采样信号的干扰,提出一种鲁棒平滑滤波算法。利用权重函数为FOG每个采样数据迭代加权,给异常值分配较低权重给高质量数据分配较高权重,有效提高了平滑滤波的鲁棒性。采用广义交叉验证估计平滑参数再利用离散余弦变换计算原始FOG数据的平滑值,提高了平滑滤波的运算速度。软件仿真和实际实验结果表明,相比传统最小二乘平滑滤波算法,所提算法能够有效抑制FOG随机噪声和异常采样信号的干扰,并且对时变的FOG信号具有较好的跟踪能力。
In order to reduce the random noise of FOG and eliminate the interference of abnormal sampled signals, a robust smoothing filtering algorithm is proposed. The weight function is used to iteratively weight each sampled data of FOG, allocate lower weights to outliers, assign higher weights to high quality data, and effectively improve the robustness of smoothing filtering. The generalized cross-validation is used to estimate the smoothing parameters and then the discrete cosine transform is used to calculate the smoothed values of the original FOG data to improve the smoothing filtering speed. Software simulation and experimental results show that the proposed algorithm can effectively suppress the interference of FOG random noise and abnormal sampling signals and has better tracking ability than time-varying FOG signals compared with the traditional least-squares smoothing filtering algorithm.