针对多功能雷达信号分选时,采用哪种复杂网络进行分选更为有效的问题,提出了一种改进的有限穿透可视图(ILPVG)算法,并采用不同的复杂网络建立算法,根据复杂网络的拓扑属性和中心性指标进行网络模型分析,然后通过标签传播算法和密度峰值聚类算法检测其社区结构得到雷达信号分选结果。仿真实验表明,在多功能雷达分选场景下,基于ILPVG算法和基于有限穿透可视图(LPVG)算法的复杂网络在网络的连通性和紧密性上都高于其他网络。ILPVG 网络通过标签传播算法得到的子社区结果、雷达分选准确率都高于其他网络,其雷达信号分选准确率比LPVG 提高了3. 46%,且明显优于基于k 均值的和具有噪声的基于密度的聚类算法的分选方法,表明了ILPVG 算法在雷达信号分选上的有效性和优越性。

In order to solve the problem of which complex network is more effective for multifunction radar signal sorting, an improved limited penetrable visual graph (ILPVG) algorithm is proposed, and different complex network building algorithms are used to analyze the network model according to the topological attributes and centrality indicators of the complex network. Then, the radar signal sorting results are obtained by detecting the community structure through label propagation algorithm and density peak clustering algorithm. Simulation results show that the complex networks based on ILPVG algorithm and limited penetrable visual graph (LPVG) algorithm are higher than other networks in terms of network connectivity and compactness in the multifunction radar sorting scenario. The sub-community results and radar sorting accuracy obtained by the ILPVG network through the label propagation algorithm are higher than those of other networks, and the radar signal sorting accuracy is 3. 46% higher than that of LPVG, and significantly better than those of sorting methods based on the k-means and density-based spatial clustering of applications with noise algorithm, which indicates the effectiveness and superiority of the ILPVG algorithm in radar signal sorting.

TN957.51

国防特色学科发展资助项目(JCKY2019415D002)