Signal local Reconstruction Algorithm based on Compressed Sensing and Unsupervised Learning

With the rapid growth of data volume of the Internet and other platforms, the bandwidth needed for data transmission and reception is getting higher and higher, and the requirements for processing speed and sampling frequency of information acquisition are also improved. Based on the Shannon sampling theory, it is found that only when the sampling frequency of the signal is higher than or equal to twice the signal bandwidth, can higher-quality analog signal recovery effect be achieved. In order to efficiently deal with the problem of the fast reconstruction of the unknown sparsity of the compressed signal, a new method with wider adaptability and higher efficiency is proposed. Firstly, use isometric rules to obtain upper and lower bounds of the compressed output signal, and take the closest integer value as estimation value of sparse signal; secondly, by reducing the number of iterative projection support of observation vector to realize complexity reduction of calculation of signal reconstruction, and design evaluation probability system for signal reconstruction to achieve implementation of the validation of the proposed index scheme; finally, based on the experimental verification, the proposed method can obtain and achieve fast reconstruction of sparsity of unknown signal and can obtain higher success rate than backtracking scheme. Introduction Danoho, Candes and other experts found and interpreted the process of compressed sensing of information based on research [1-3]. The main idea of this paper is to reduce the computational complexity of the algorithm starting from the simplified reconstruction projection process by using the compressed sensing process. In order to solve the problems mentioned above, this paper designs a new method of signal reconstruction (OASP) which is more adaptive and efficient. In this method, the sparse prediction algorithm is used to achieve reconstruction of the information of unknown sparsity. At the same time, it can effectively avoid the redundant computation of the iterative support set projection of the observation vector in the reconstruction process to reduce the computational complexity of the reconfiguration strategy. Then, evaluation index design of the performance of reconstruction of information on the basis of in-depth study of the existing methods of measuring is performed to get access to a more scientific and comprehensive assessment. The experimental analysis shows that the sampling frequency standard of the process is much lower than the Nyquist standard, and the original signal reconstruction results obtained by this method have higher reliability. Strategy Description As previously mentioned, because the present reconstruction strategy only applies to reconstruction of signal with known sparsity, but not applicable to signal with unknown sparsity, this paper makes improvement on reconstruction strategy to combine sparsity fast estimation strategy for implementation of efficiency promotion of calculation process of sparsity and realization of unbiased sparsity estimation. In addition, in the proposed reconstruction strategy, projection process of observation vector iterative candidate set of backtracking strategy is improved to obtain sufficient convergence and avoid the support set projection calculation, so as to realize the simplification of reconstruction process. 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering Research, volume 123