一种用于光纤陀螺降噪的鲁棒平滑滤波算法

A robust smoothing filter algorithm for de-noising of fiber optic gyroscope

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 fiber optic gyroscope （FOG） and eliminate the disturbance from singular sampled signal, a robust smoothing filtering algorithm was proposed. Each sampled data of FOG were iteratively weighted by weighting function with giving outliers a low weight and allocating a relatively high weight to high quality data. Then the robustness of smoothing filter can be improved evidently. The operation speed of smoothing filtering algorithm is increased effectively by generalized cross validation （GCV） and discrete cosine transform （DCT）. Simulation and practicable test results show that the proposed algorithm can restrain the interference by singular sampled signal or random noise of FOG and has better signal tracking capability.