Recent advances in sparse representation of non-stationary signal
Wang Fang-mei · Journal of Computer Applications · 2012
Signal decomposition is a process that obtains information from signals and it is a foundational and key technique for many fields such as pattern recognition,intelligent system and machinery fault diagnosis.It is very important to study non-stationary signal decomposition which always includes lots of information that can reflect the changing of the system and widely exists.After improving the sparsity of signal representation,the engineering background of feature extraction for non-stationary signal was studied in this paper,the characteristics,mechanisms,development history and current and future challenges of five types of methods were analyzed in depth,the models of these methods were compared,together with the state-of-the-art of feature extraction models in signal processing and analysis and some successful applications available were systematically reviewed.Finally,several main problems and a few deficiencies were pointed out,and future research directions were anticipated.