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Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

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Geo-Spatial Information Science 2006年第3期9卷 187-190页

作者： WANG Jian GAO Jingxiang XU Changhui School of Environment and Spatial Informatics China University of Mining and Technology Zhaishan Quanshan District Xuzhou 221008 China Institute of Photogrammetry and Remote Sensing CASM Beijing 100039 China.

Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

关键词： noise single epoch GPS deformation signal mallat algorithm gross error detection gross error recovery

Restoration of Noisy 1/f Family Fractal Signal Based on Haar Wavelets and Wiener Filter

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Journal of Electronic Science and Technology of China 2003年第1期1卷 69-73页

作者： CAO Kunyong YU Shenglin(College of Automation Engineering,Nanjing University of Aeronautics and Astronautics Nanjing 210016 China) College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

In order to restore noisy fractal Brownian motion(FBM),discrete fractional gaussiannoise(DFGN) combined with noise increments is decomposed by Haar wavelets based on *** the correlation of detail coefficients,a bank of Wiener filters are used to estimatethe detail coefficients to reconstruct DFGN considering the estimated approximation coefficients in thecoarsest scale in the minimum mean square ***,the reconstructed DFGN is used to restore *** the digital simulation,in light of the restoration mean square error,we show that the suppose that thecorrelation of detail coefficients and the approximation coefficients in the coarsest scale for any Hurstcould be avoided is ***,we calculate the estimation root mean square error of the hurstparameter of the restored FBM to show the validity of our algorithm.

关键词： 1/f family fractal signals fractal Brownian motion wiener filter mallat algorithm

Implementation of Adaptive Wavelet Thresholding Denoising algorithm Based on DSP

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Journal of Measurement Science and Instrumentation 2011年第3期2卷 272-275页

作者：张雪峰康春霞裴峰张志杰 Key Laboratory of Instrumentation Science & Dynamic Measurement(Ministry of Education) North University of China

By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal *** on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding *** threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is *** simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.

关键词： mallat algorithm wavelet denoising thresholding function adaptive threshold Digital Signal Processors