Abstract
In order to meet the needs of large number of overlapping samples in sequential fine feature extraction, this work proposes a frame by frame (segment) iterative algorithm based on the “sparse” feature of incremental data to achieve efficient real-time calculation of large FFT, which breaks through the hardware bottleneck of real-time computing platform such as DSP platform in high-resolution spectral analysis. The proposed algorithm makes use of the sparseness of incremental data to make the computation burden of segment iteration lighter than that of standard FFT, and has the potential of parallel computation. The simulation results show that the new algorithm is more efficient than the standard FFT when the number of segments is large.
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