Chen Xiang, Li Jingchao, Han Hui, Ying Yulong
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang, Henan 471003, People's Republic of China.
College of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, People's Republic of China.
R Soc Open Sci. 2018 May 2;5(5):180087. doi: 10.1098/rsos.180087. eCollection 2018 May.
Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. 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.
针对传统分形盒维数算法在辐射源信号细微特征提取方面的局限性，提出了一种双改进广义分形盒维数特征向量算法。首先，对辐射源信号进行预处理，通过希尔伯特变换获取信号的瞬时幅度。然后，提取信号瞬时幅度的改进分形盒维数作为第一特征向量。同时，提取未经过希尔伯特变换的信号的改进分形盒维数作为第二特征向量。最后，双改进分形盒维数特征向量构成多维特征向量作为信号细微特征，通过灰色关联算法用于辐射源信号识别。实验结果表明，与传统分形盒维数算法和单改进分形盒维数算法相比，所提双改进分形盒维数算法能更好地提取不同重构相空间下信号的细微分布特征，具有较好的识别效果和实时性。
