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

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.

Revealing the Environmental Characteristics of Towns in the Middle Himalayas Using a Geographic Information System and Self-Organizing Map

“…To overcome the subjectivity and low automation of methods based on indicator thresholds or feature interpretation, some scholars have proposed unsupervised classification methods based on self-organizing feature mapping neural network (SOFM) models and clustering methods [40][41][42]. Unsupervised classification models can divide a large number of unlabeled samples into several categories according to the "birds of a feather flock together" principle, based on the similarity of their data features [43,44]. Since the town environment is the result of many driving factors, the use of GIS and SOM can quickly integrate terrain, climate and soil, water and land transport conditions, and other environmental factors to effectively identify town environmental characteristics.…”