A Sorting Method of SAR Emitter Signal Sorting Based on Self-Supervised Clustering

Author:

Dai Dahai1,Qiao Guanyu2,Zhang Caikun2,Tian Runkun1,Zhang Shunjie1

Affiliation:

1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

2. State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information Systems, Luoyang 471003, China

Abstract

Most existing methods for sorting synthetic aperture radar (SAR) emitter signals rely on either unsupervised clustering or supervised classification methods. However, unsupervised clustering can consume a significant amount of computational and storage space and is sensitive to the setting of hyperparameters, while supervised classification requires a considerable number of labeled samples. To address these limitations, we propose a self-supervised clustering-based method for sorting SAR radiation source signals. The method uses a constructed affinity propagation-convolutional neural network (AP-CNN) to perform self-supervised clustering of a large number of unlabeled signal time-frequency images into multiple clusters in the first stage. Subsequently, it uses a self-organizing map (SOM) network combined with inter-pulse parameters for further sorting in the second stage. The simulation results demonstrate that the proposed method outperforms other depth models and conventional methods in the environment where Gaussian white noise affects the signal. The experiments conducted using measured data also show the superiority of the proposed method in this paper.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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