A Novel Local Transform Inverse S-Transform Algorithm for Statistical Filter

2017 3rd International Conference on Applied Materials and Manufacturing Technology

Yin, Baiqiang^1,2,3 ; Sun, Zhanfeng^1 ; Yi, Zhong^3 ; He, Yigang^1

School of Electrical and Automation Engineering, Hefei University of Technology, Hefei

230009, China^1

Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchong

637009, China^2

Beijing Institute of Spacecraft Environment Engineering, Beijing

100094, China^3

关键词: Conventional methods;    Inverse S-transforms;    S transforms;    Statistical filtering;    Statistical properties;    Stochastic noise;    Time frequency;    Time-frequency localization;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/242/1/012118/pdf
DOI  :  10.1088/1757-899X/242/1/012118

来源: IOP

【 摘 要 】

S-transform (ST) is a useful tool for time-frequency filter. However, the conventional inverse S-transform (IST) algorithm suffers from time or frequency leakage. In this paper, we proposed a novel local transform inverse S-Transform (LTIST) algorithm for statistical filter. First, the matrix S-transform (MST) and MIST are derived. Then the proposed LTIST approach applies to denoising. The statistical property of stochastic noise in the MIST is discussed. The results show that the proposed MIST algorithm has better time-frequency localization in statistical filtering than the conventional methods. Illustrative examples verify the effectiveness of the proposed algorithm.