Abstract:The accurate identification of the working mode of multi-function phased array radar can provide a basis for electronic countermeasure decision-making, which is of great research significance. The existing working pattern recognition methods are mainly based on the training set of known labels, but in practice, there is a lack of prior information, and the data labels are difficult to obtain, which greatly affects the performance of working pattern recognition. In this paper, a working pattern recognition method based on semi-supervised learning to achieve unknown data labeling with the help of a small amount of prior information is proposed. Firstly, the performance of AP clustering(affinity propagation clustering), DBSCAN(density-based spatial clustering of applications with noise) and FCM(fuzzy C-means clustering, FCM) is compared and analyzed according to the internal evaluation index and external evaluation index of the clustering algorithm, and it is applied to the data annotation of the intercepted data after the optimal performance of the AP clustering algorithm is verified. Then, the convolutional neural network is used to identify the radar working mode and compare it with the network under the known label training set, which verifies the feasibility of data annotation based on AP clustering algorithm, improves the noise immunity compared with the traditional recognition network, and provides a basis for subsequent multi-functional radar behavior cognition.
