Abstract
An effective de-noising method for fiber optic gyroscopes (FOGs) is proposed. This method is based on second-generation Daubechies D4 (DB4) wavelet transform (WT) and level-dependent threshold estimator called Stein’s unbiased risk estimator (SURE). The whole approach consists of three critical parts: wavelet decomposition module, parameters estimation module and SURE de-noising module. First, DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module. Second, in the parameters estimation module, maximum likelihood estimation (MLE) is used for stochastic noise parameters estimation. Third, combined with soft threshold de-noising technique, the SURE de-noising module is designed. For comparison, both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated. The experiment results show that the computation cost is 40% less than that of the traditional wavelet method. The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk, bias instability and quantization noise are reduced to 0.007 2° /ℴh, 0.004 1° / h, and 0.008 1°, respectively.
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Foundation item: Supported by the Aerospace Science and Technology Innovation Foundation of China (2006)
Biography: DANG Shuwen (1980–), female, Ph. D. candidate, research direction: inertial navigation technique and nonlinear filtering.
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Dang, S., Tian, W. & Jin, Z. De-noising stochastic noise in FOG based on second-generation DB4 wavelet and SURE-threshold. Wuhan Univ. J. Nat. Sci. 14, 494–498 (2009). https://doi.org/10.1007/s11859-009-0607-9
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DOI: https://doi.org/10.1007/s11859-009-0607-9
Key words
- second-generation wavelet
- stochastic noise
- fiber optic gyroscope (FOG)
- Stein’s unbiased risk estimator (SURE)
- soft threshold