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Gabor变换在信号处理领域一直被认为是一十分有用的时频分析工具,却因Gabor变换算法的高计算复杂性而限制了其实时应用.本文基于多抽样率滤波原理,设计了分析和综合滤波器组分别用于实现离散Gabor变换与展开,从而提出了全新的离散Gabor展开与变换快速并行算法.所设计的分析和综合滤波器组中的每一并行通道具有一致的结构并能够利用快速Fourier变换(FFT)及其逆变换(IFFT)减小计算量.每一并行通道计算复杂性非常小,只取决于输入离散信号的长度及Gabor频率抽样点数,并且每一并行通道计算复杂性不会随Gabor变换过抽样率增加而增大.本文对所提出的并行算法的计算复杂性进行了分析并与目前主要的离散Gabor展开与变换并行算法进行了比较,结果表明所提出基于多抽样率滤波实现离散Gabor展开与变换的并行算法对实时信号处理十分有利.
Gabor transform has been considered as a very useful time-frequency analysis tool in the field of signal processing, but its real-time application is limited due to the high computational complexity of Gabor transform algorithm.Based on the multi-sample rate filtering principle, Gabor transform is designed to analyze and synthesize Filter banks are used to implement discrete Gabor transform and expansion, respectively, and a new fast parallel algorithm for discrete Gabor expansion and transform is proposed. Each parallel channel in the analysis and synthesis filter bank has a uniform structure and can utilize fast Fourier Transform (FFT) and its Inverse Transform (IFFT) to reduce the computational complexity of each parallel channel calculation complexity is very small, only depends on the length of the input discrete signal and Gabor frequency sampling points, and each parallel channel computational complexity is not Will increase with the increase of sampling rate of Gabor transform.This paper analyzes the computational complexity of the proposed parallel algorithm and compares it with the main parallel discrete Gabor expansion and transform parallel algorithm.The results show that the proposed sampling rate based on multiple sampling rate The parallel algorithm of filtering to realize discrete Gabor expansion and transformation is very beneficial to real-time signal processing.