基于区域像素密度星座图的自动调制识别方法  

Automatic modulation classification method via regional pixel density constellation diagram

作　　者：潘求凯 杨淑媛 冯志玺 PAN Qiukai;YANG Shuyuan;FENG Zhixi(The School of Artificial Intelligence,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学人工智能学院,西安710071

出　　处：《空间电子技术》2024年第6期9-15,共7页Space Electronic Technology

基　　金：国家自然科学基金项目(编号:U22B20218,62171357,62276205)。

摘　　要：星座图是调制识别技术中被广泛使用的特征表达方式,目前已经发展出多种针对星座图的增强方法用于提升其表征效果。然而,大部分方法未能准确提取不同星座区域的星座点密度,对不同密度星座簇的对比增强效果有限。为了克服上述缺陷,本文提出了一种区域像素密度星座图增强方法,通过统计每个小网格区域的灰度像素密度,更加准确反映不同星座簇的密度。更进一步,通过分段像素阈值对像素值进行重置,提高了增强星座图的视觉对比效果。此外,本文设计了配套的深度特征聚合注意力网络(DFAAN),通过聚合不同层级的语义特征提高了对调制类型判别的鲁棒性。实验结果表明,本文提出的方法即使在具有相偏、频偏以及低信噪比的23种调制方式数据集上,OA比其他方法至少高出11.26%、AA至少高出8.65%、Kappa系数至少高出11.77%。Constellation diagram is a widely used feature expression method in automatic modulation classification technology.At present,a variety of constellation diagram enhanced methods have been developed to improve its representation effectiveness.However,most of the methods can not accurately extract the constellation point density in different constellation regions,and the contrast enhancement effect on constellation clusters with different densities is limited.In order to overcome the above defects,this paper proposes a regional pixel density constellation diagram enhanced method.By calculating the grayscale pixel density in each small grid region,our method more accurately reflects the density of different constellation clusters.Furthermore,the pixel value is reset by the segmented pixel threshold,which greatly improves the visual contrast effect of the enhanced constellation diagram.In addition,this paper designs a complementary Deep Feature Aggregation Attention Network(DFAAN),which significantly enhances the robustness of modulation type classification by aggregating semantic features from different levels.The experimental results show that the proposed method outperforms other approaches on the dataset with 23 modulation types,even under phase offset,frequency offset,and low SNR conditions.Specifically,the OA is at least 11.26%higher,the AA is at least 8.65%higher,and the Kappa coefficient is at least 11.77%higher compared to other methods.

关 键 词：自动调制识别 区域像素密度星座图 深度特征聚合注意力网络 

分 类 号：V474[航空宇航科学与技术—飞行器设计] V443