Research On Measurement Signal Processing Technology Based On Wavelet Analyse

To get an accurate and real-time process result is always a best target what people hope. The Wavelet analysis theory is gradually developing to become one of important technology in the dynamic measurement signal process field by its advantage of multiresolution and multidimension analysis on time-frequent.However, there are many problems which still limit the practice application of Wavelet analysis theory, such as the problem how choose the rightist Wavelet base function and the best threshold value, shortage of the directionality and the shift invariance due to classics DWT, and so on. How to solve these problems has been the key that ensure the Wavelet analysis theory to become utility in practical applications. It must show great academic value and practical importance.Through collecting and studying of many documents about Wavelet analysis theory, this thesis makes studies of the characteristic of Wavelet base function, the character of Wavelet Transform and the property of correlation dynamic measurement signal. Then, some effective resolving methods and the accurate real-time improvement algorithms are proposed, the correctness of this idea is testified by some numerical experiments.The basis properties of various wavelet families are first introduced. Seven measures are then studied for base wavelet selection. To resolve a conflict that the results obtained from these measures are not consistent, two comprehensive optimization index, namely the maximum energy-to-Shannon entropy ratio and the minimum minmax information measure, are developed and evaluated for appropriate base wavelet selection. Numerical experiment shows they properly meet with need of base wavelet selection.The denoising mechanism of Wavelet Transform is detailed analysis, process and characteristic of the classics Wavelet-based denoising technology is studied. Because the Wavelet-based Threshold value deniosing can reach similar optimum on minimum mean square error, and framelet transform can remedy some wavelet coefficients with vital information. So a framelet denoising technology with the improvement SURE-LET based on Wavelet-based Threshold value deniosing is proposed. Numerical experiment shows it can seek global optimum by self- adapting and imply denoising based on keep the signal completeness.The various maximum-modulus of Wavelet Transform-based signal detection technology are detailedly studied and found not to detect for faint signal. Following coefficient correlation of Wavelet Transform-based, Wavelet entropy is lead into signal detection technology field, and choosing complex Morlet as the rightest Wavelet base function. So a complex Morlet Wavelet-based Wavelet entropy detection technology for faint signal is proposed. Numerical experiment shows the effects of this algorithm obvious surpass over maximum-modulus of Wavelet Transform-based signal detection technology.By studying and analysing the various standard linearity Wavelet-based image signal fusion technology detailedly, a maximum lifting scheme-based undecima- ted nonlinearity discrete wavelet transform signal fusion technology is proposed. First maximum lifting scheme is designed according to maximum-modulus of Wavelet Transform. Then the shift invariance expansion is carried out by Noble Identities theorem based on maximum lifting scheme. At last fusion rule is settled. The numerical experiment shows the merit of this algorithm is only involving in integer calculation, and better for the hardware realization.According to the concepts of Human Visual System(HVS), the weight for each subband is chosen to reflect its perceptual impact on the image, which measures the distortions in the global structure and local details of an image in a more balanced way automatically. So a Image Quality Metric(IQM)called Weighted Normalized Mean Square Error of wavelet subbands(WNMSE)is proposed. WNMSE uses the weighted sum of the normalized mean square errors of wavelet coefficients to assess the quality of an image, and can be calculated in the middle of compression without reconstructing the image. Numerical experiment shows that WNMSE has better performance than both the legacy Peak Signal-to- Noise Ratio(PSNR)and the well referenced Structural SIMilarity(SSIM).