基于PSO-DBSCAN和SCGAN的未知雷达信号处理方法

针对雷达实际侦察过程中会侦收到大量样本库中所没有的未知雷达信号,设计了一种基于粒子群优化的具有噪声的密度聚类算法和半监督条件生成对抗网络的单脉冲未知雷达信号处理方法.通过粒子群优化算法得到具有噪声的密度聚类算法的最优输入参数后,对未知雷达信号进行聚类,在聚类算法输出的簇中采用距离筛选算法筛选出更为可信的样本将其扩展到雷...

Unknown radar signal processing based on PSO-DBSCAN and SCGAN

Aiming at the actual radar reconnaissance process that will receive unknown radar signals that are not available in a large number of sample libraries, this paper designs density based spatial clustering of applications with noise based on particle swarm optimization (PSO-DBSCAN) and a semi-supervised conditional generation adversarial network (SCGAN) to process the monopulse unknown radar signals. First, the optimal input parameters of the noisy density clustering algorithm are obtained through the particle swarm optimization (PSO), and then the unknown radar signals are clustered, and the distance filtering algorithm is used to filter out more credible samples from the clusters output by the clustering algorithm, which are extended to the radar sample library. When too many types of unknown radar signals are added, the feature extraction network needs to be expanded and trained, and the small amount of data in the sample library is difficult to support the feature extraction network for effective expansion training. Therefore, a semi-supervised conditional generation confrontation network is designed to realize the training and classification of unknown signals in the case of small samples. The simulation results show that this method performs well in the recognition of unknown radar signals.