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 ...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 Kalman filter(CKF).展开更多
文摘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 Kalman filter(CKF).
