Radar signal processing based on Sparse Fast Fourier Transform

2021 ◽  
Author(s):  
Xiaojuan Bai ◽  
Hao Tian ◽  
Lu Guan
Author(s):  
Hadhrami Ab Ghani ◽  
Mohamad Razwan Abdul Malek ◽  
Muhammad Fadzli Kamarul Azmi ◽  
Muhammad Jefri Muril ◽  
Azizul Azizan

Fast Fourier Transform has long been established as an essential tool in signal processing. To address the computational issues while helping the analysis work for multi-dimensional signals in image processing, sparse Fast Fourier Transform model is reviewed here when applied in different applications such as lithography optimization, cancer detection, evolutionary arts and wasterwater treatment. As the demand for higher dimensional signals in various applications especially multimedia appplications, the need for sparse Fast Fourier Transform grows higher.


2014 ◽  
Vol 1049-1050 ◽  
pp. 1245-1248
Author(s):  
Yan Xin Yu ◽  
Chun Yang Wang ◽  
Yu Chen ◽  
Hong Yan Sun

Linear canonical transformation is a new signal processing tools developing in recent years. As a unified multi-parameter linear integral transform, linear canonical transformation has its unique advantages when dealing with non-stationary signal. However, from the existing literatures, the basic theoretical system is not perfect, some of the theories associated with signal processing needs to be further established or strengthened, the research of linear canonical transformation has important theoretical significance and practical significance, but linear canonical transformation needs a lot of calculation, it is not like Fourier transform, fractional Fourier transform, Fresnel transform and scale operator, they have already been widely used in various fields of expertise, in order to reduce the amount of calculation, this paper puts forward a fast algorithm which uses duality theorem of linear canonical transformation to reduce the amount of calculation, it can quickly complete the operation when we use linear canonical transformation to process the signal during radar signal processing, the time for normal algorithm is 5s, the fast algorithm needs only 0.2s.


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