Multi-Function Radar Signal Sorting Based on Complex Network

In complex electromagnetic environments, the challenge of signal sorting task for multi-function radars (MFRs) with various work modes has arisen. The previous methods are prone to cause the so-called “increasing batch” problem, which means that the work modes of one MFR may be sorted into multiple emitters. In this letter, a MFR signal sorting method based on complex network is proposed to tackle the problem mentioned above. The novel method utilizes limited penetrable visibility graph to construct the network from interleaved radar pulse sequences, then employs label propagation algorithm and density peak clustering to detect community structures, thus fulfilling deinterleaving of pulses from several MFRs. Simulation results show that the proposed method is effective to alleviate the “increasing batch” problem, and is also robust under non-ideal conditions.

[1]  Mohaned Giess Shokrallah Ahmed and Bin Tang Sorting radar signal from symmetry clustering perspective , 2010 .

[2]  Jinjun Tang,et al.  Characterizing traffic time series based on complex network theory , 2013 .

[3]  Veerendra Dakulagi,et al.  Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection , 2020, IEEE Access.

[4]  Ali Kara,et al.  Improvements on deinterleaving of radar pulses in dynamically varying signal environments , 2017, Digit. Signal Process..

[5]  Yinghong Ma,et al.  A three-stage algorithm on community detection in social networks , 2020, Knowl. Based Syst..

[6]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[8]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  H. K. Mardia New techniques for the deinterleaving of repetitive sequences , 1989 .

[10]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Kai Wang,et al.  Weighted complex networks in urban public transportation: Modeling and testing , 2020 .

[12]  Gong Jian,et al.  An Improved Algorithm for Deinterleaving of Radar Pulses , 2001 .

[13]  Yilun Shang Generalized K-Core Percolation in Networks with Community Structure , 2020, SIAM J. Appl. Math..

[14]  Masaaki Kobayashi,et al.  Improved algorithm for estimating pulse repetition intervals , 2000, IEEE Trans. Aerosp. Electron. Syst..

[15]  Changbo Hou,et al.  Multi-threshold fuzzy clustering sorting algorithm , 2017, 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS).

[16]  Antonio De Maio,et al.  Intrapulse radar-embedded communications via multiobjective optimization , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Subha D. Puthankattil,et al.  Complex network analysis of MCI-AD EEG signals under cognitive and resting state , 2020, Brain Research.

[18]  Eduardo Gutiérrez de Ravé,et al.  A sliding window-based algorithm for faster transformation of time series into complex networks. , 2019, Chaos.

[19]  Zhou Ting-Ting,et al.  Limited penetrable visibility graph for establishing complex network from time series , 2012 .

[20]  Fan Fu-hua,et al.  Application of Cluster Method to Radar Signal Sorting , 2004 .

[21]  Duyan Bi,et al.  Multiple-Parameter Radar Signal Sorting Using Support Vector Clustering and Similitude Entropy Index , 2014, Circuits Syst. Signal Process..

[22]  Minggang Wang,et al.  Carbon price forecasting with complex network and extreme learning machine , 2020 .

[23]  Yan Zhang,et al.  Density-Based Fuzzy C-Means Multi-center Re-clustering Radar Signal Sorting Algorithm , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[24]  Sajid Ahmed,et al.  A survey of correlated waveform design for multifunction software radar , 2016, IEEE Aerospace and Electronic Systems Magazine.

[25]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[26]  N. J. Whittall Signal sorting in ESM systems , 1985 .

[27]  Rianne Jacobs,et al.  Tracing the Origin of Food-borne Disease Outbreaks , 2020, Epidemiology.

[28]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.