Signal Subtle Feature Extraction Algorithm Based on Improved Fractal Box-Counting Dimension

Aiming at the limitations of traditional fractal box-counting dimension algorithm in the application of subtle feature extraction of radiation source signals, an improved generalized fractal box-counting dimension algorithm is proposed in the paper. Firstly, the signal is preprocessed, and the real and imaginary data of the signal after Hilbert transform is extracted to obtain the instantaneous amplitude of the signal; Then, the improved fractal box-counting dimension of signal instantaneous amplitude is extracted as the first eigenvector; At the same time, the improved fractal box-counting dimension of the signal without Hilbert transform is extracted as the second eigenvector; Finally, the dual improved fractal box-counting dimension eigenvectors form the multi-dimensional eigenvectors to form a new fractal box-counting dimension eigenvector as signal subtle features, for radiation source signal recognition. By establishing a dual improved fractal box-counting dimension feature space, 11 different practical radiation source signals are classified, compared with the traditional box-counting dimension algorithm, and the recognition rate is calculated. 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 reflect the signal subtle distribution characteristics under the different reconstruction phase space, and has a better recognition effect with good real-time performance.

[1]  Xiaozhi Hu,et al.  Effects of heterogeneity and load amplitude on fatigue rate prediction of a welded joint , 2016 .

[2]  Shaojiang Dong,et al.  Rotating Machine Fault Diagnosis Based on Locality Preserving Projection and Back Propagation Neural Network–Support Vector Machine Model , 2015 .

[3]  Yu Yang,et al.  The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis , 2015 .

[4]  Jiantao Zhou,et al.  Distribution of primary additional errors in fractal encoding method , 2014, Multimedia Tools and Applications.

[5]  S. Cross,et al.  RAD51B in Familial Breast Cancer , 2016, PloS one.

[6]  Zheng Pan,et al.  A NOVEL FAST FRACTAL IMAGE COMPRESSION METHOD BASED ON DISTANCE CLUSTERING IN HIGH DIMENSIONAL SPHERE SURFACE , 2017 .

[7]  Jingchao Li,et al.  A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory , 2016, PloS one.

[8]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[9]  Chao Wang,et al.  A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks , 2016, Sensors.

[10]  Bill McKelvey,et al.  A COMPARISON OF THREE HURST EXPONENT APPROACHES TO PREDICT NASCENT BUBBLES IN S&P500 STOCKS , 2016, 1601.04188.

[11]  Haolin Li,et al.  A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine , 2016 .

[12]  A. K. Wadhwani,et al.  Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis , 2011 .

[13]  Long Tang,et al.  Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines , 2015, Sensors.

[14]  Jian Guo,et al.  Study on rolling bearing on-line reliability analysis based on vibration information processing , 2017, Comput. Electr. Eng..

[15]  Jingchao Li.  A New Robust Signal Recognition Approach Based on Holder Cloud Features under Varying SNR Environment , 2015, KSII Trans. Internet Inf. Syst..

[16]  Jingchao Li.  A Novel Recognition Algorithm Based on Holder Coefficient Theory and Interval Gray Relation Classifier , 2015, KSII Trans. Internet Inf. Syst..

[17]  Qiong Chen,et al.  Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation , 2013 .

[18]  Jian Guo,et al.  Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis , 2016 .

[19]  D D Zhang.  Bearing fault diagnosis based on the dimension–temporal information , 2011 .

[20]  Jin Chen,et al.  Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm , 2015 .

[21]  Jian Guo,et al.  A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals , 2015 .