Data from: Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong (2018), Data from: Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors, Dryad, Dataset, https://doi.org/10.5061/dryad.567mc91
Aiming at the limitation of traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm was proposed in the paper. Firstly, the radiation source signal was preprocessed, and Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors form the multi-dimensional eigenvectors as signal subtle features, used for radiation source signal recognition by the gray relation algoritm. The experimental results show that compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.