Computing the Fourier Transformation over Temporal Data Streams (Invited Talk)

In radio astronomy the sky is continuously scanned to collect frequency information about celestial objects. The inverse 2D Fourier transformation is used to generate images of the sky from the collected frequency information. We propose an algorithm that incrementally refines images by processing frequency information as it arrives in a temporal data stream. A direct implementation of the refinement with the discrete Fourier transformation requires O(N2) complex multiplications to process an element of the stream. We propose a new algorithm that avoids recomputations and only requires O(N) complex multiplications. 2012 ACM Subject Classification Information systems → Stream management; Theory of computation → Data structures and algorithms for data management

[1]  Sung-Jea Ko,et al.  The Hopping Discrete Fourier Transform [sp Tips&Tricks] , 2014, IEEE Signal Processing Magazine.

[2]  Krzysztof Duda,et al.  Accurate, Guaranteed Stable, Sliding Discrete Fourier Transform [DSP Tips & Tricks] , 2010, IEEE Signal Processing Magazine.

[4]  Chun-Su Park,et al.  Fast, Accurate, and Guaranteed Stable Sliding Discrete Fourier Transform [sp Tips&Tricks] , 2015, IEEE Signal Processing Magazine.

[5]  D. M. Monro,et al.  Moving discrete Fourier transform , 1992 .

[6]  E. Jacobsen,et al.  The sliding DFT , 2003, IEEE Signal Process. Mag..

[7]  Barry G. Sherlock,et al.  Windowed discrete Fourier transform for shifting data , 1999, Signal Process..

[8]  Peter D. Welch,et al.  Fast Fourier Transform , 2011, Starting Digital Signal Processing in Telecommunication Engineering.