Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

Allan variance (AV) stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme. However, the latter usually employs a traditional hard threshold or soft threshold that presents some mathematical problems. An adaptive dual threshold...

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Bibliographic Details
Published inShanghai jiao tong da xue xue bao. Yi xue ban Vol. 25; no. 4; p. 434
Main Authors Bessaad, Nassim, Bao, Qilian, Sun, Shuodong, Du, Yuding, Liu, Lin, Hassan, Mahmood Ul
Format Journal Article
LanguageChinese
Published Shanghai Shanghai Jiaotong University Press 01.08.2020
Subjects
Algorithms
Discrete Wavelet Transform
Inertial navigation
Inertial sensing devices
Kalman filters
Mathematical problems
Navigation systems
Noise reduction
Stochastic models
Stochastic processes
Stochasticity
Thresholds
Tuning
Wavelet transforms
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Summary:Allan variance (AV) stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme. However, the latter usually employs a traditional hard threshold or soft threshold that presents some mathematical problems. An adaptive dual threshold for discrete wavelet transform (DWT) denoising function overcomes the disadvantages of traditional approaches. Assume that two thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two thresholds assure continuity and overcome previous difficulties. On the basis of AV, an application for strap-down inertial navigation system (SINS) stochastic model extraction assures more efficient tuning of the augmented 21-state improved exact modeling Kalman filter (IEMKF) states. The experimental results show that the proposed algorithm is superior in denoising performance. Furthermore, the improved filter estimation of navigation solution is better than that of conventional
ISSN:1674-8115