Application of multiple-scale variable step least mean square adaptive algorithm to fiber optic gyroscope data processing
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摘要: 为有效抑制光纤陀螺(FOG)随机噪声，提出将一种多尺度变步长最小均方(MVSLMS)自适应算法应用于FOG数据处理中。根据FOG输出数据的特点，构建了MVSLMS自适应滤波器，提出了FOG信号滤波算法的实现步骤。对FOG实测静态数据、振动数据和速率测试数据进行了滤波实验，结果表明所提算法对FOG随机噪声的抑制效果明显，相比LMS滤波，MVSLMS自适应滤波后的静态数据零偏稳定性数值减小了72.0%，振动数据在振前、振中、振后零偏稳定性数值分别减小91.5%,77.4%和96.5%，速率测试数据标准差减小了54.4%。摇摆测试滤波实验结果表明所用算法对FOG真值信号具有较好的跟踪能力。Abstract: In order to get a better filtering result for the output of data of fiber optic gyroscope (FOG), an adaptive LMS Algorithm with Variable Step and Multi-scale Wavelet Transform(MVSLMS) was applied to FOG data processing. A MVSLMS filter was constructed on the basis of the characteristics of FOG data, and the specific implementation steps were proposed. The measured static data, vibration test data and rate test data of FOG were filtered. The experimental results show that the proposed algorithm significantly inhibits the random noise of FOG. Compared with the conventional LMS algorithm, after filtered with new algorithm, the zero drift stability of FOG output data is decreased by 72% under static condition, 91.5%, 77.4% and 96.5% before vibration, in vibration and after vibration. The standard deviation of FOG output data is decreased by 54.4% under rate test condition. The filter results under rocking motion condition confirmed that the proposed algorithm has better signal tracking capability.
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