Open-MUSIC: An Open Set Recognition Algorithm of Electromagnetic Target Based on Metric Learning and Feature Subspace Projection

YANG Liu; LI Qiang; SHAO Huai-zong

doi:10.12263/DZXB.20210829

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Open-MUSIC: An Open Set Recognition Algorithm of Electromagnetic Target Based on Metric Learning and Feature Subspace Projection

Intelligent game of electromagnetic spectrum | 更新时间：2025-12-08

- Open-MUSIC: An Open Set Recognition Algorithm of Electromagnetic Target Based on Metric Learning and Feature Subspace Projection
- ACTA ELECTRONICA SINICA Vol. 50, Issue 6, Pages: 1310-1318(2022)
- 作者机构：
  
  电子科技大学信息与通信工程学院，四川成都 611731
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210829
  CLC： TN911.7;TP391
- Received：01 July 2021，
  
  Revised：2022-01-02，
  
  Published：25 June 2022
- 稿件说明：
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杨柳,利强,邵怀宗.Open-MUSIC：基于度量学习与特征子空间投影的电磁目标开集识别算法[J].电子学报,2022,50(06):1310-1318.

YANG Liu,LI Qiang,SHAO Huai-zong.Open-MUSIC: An Open Set Recognition Algorithm of Electromagnetic Target Based on Metric Learning and Feature Subspace Projection[J].ACTA ELECTRONICA SINICA,2022,50(06):1310-1318.
杨柳,利强,邵怀宗.Open-MUSIC：基于度量学习与特征子空间投影的电磁目标开集识别算法[J].电子学报,2022,50(06):1310-1318. DOI： 10.12263/DZXB.20210829.

YANG Liu,LI Qiang,SHAO Huai-zong.Open-MUSIC: An Open Set Recognition Algorithm of Electromagnetic Target Based on Metric Learning and Feature Subspace Projection[J].ACTA ELECTRONICA SINICA,2022,50(06):1310-1318. DOI： 10.12263/DZXB.20210829.

摘要

在越来越复杂的电磁频谱环境中，要想实现对频谱资源的管控，首先要判断发送信号的辐射源是否是己方已知的.针对此问题，本文提出了一种基于计算特征子空间投影比值的算法Open-MUSIC（MUltiple SIgnal Classification），通过神经网络获得已知类特征表示；进而得到已知类特征矩阵的两个正交子空间；以特征在两个子空间内的投影比值为指标，对辐射源信号样本是否为已知做判决.在3个数据集上的仿真表明，Open-MUSIC算法的性能在电磁数据集上较其他方法提升了3%以上.

Abstract

In the increasingly complex electromagnetic spectrum environment

in order to realize the management and control of spectrum resources

it is necessary to determine whether the received signal is from the known or unknown radiation source. To tackle this problem

this paper proposes an algorithm named Open-MUSIC(MUltiple SIgnal Classification) to discriminate the known and unknown sources. The key idea of Open-MUSIC is to form the feature space from the known classes via a judiciously designed neural network

and then the feature space is decomposed into two orthogonal subspaces

namely the range subspace and the null subspace. Based on the projection ratio of the test signal's feature onto the two subspaces

we can accurately discriminate the known and the unknown radiation sources. Experiments on three datasets show that the performance of the Open-MUSIC is improved by more than 3% on electromagnetic data sets compared to other methods.

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references

BENDALE A , BOULT T E . Towards open set deep networks [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE , 2016 : 1563 - 1572 .

BENDALE A , BOULT T . Towards open world recognition [C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston : IEEE , 2015 : 1893 - 1902 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM , 2017 , 60 ( 6 ): 84 - 90 .

O'SHEA T J , ROY T , CLANCY T C . Over-the-air deep learning based radio signal classification [J]. IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 168 - 179 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE , 2016 : 770 - 778 .

PENG S L , JIANG H Y , WANG H X , et al . Modulation classification based on signal constellation diagrams and deep learning [J]. IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 3 ): 718 - 727 .

DUAN S R , CHEN K , YU X , et al . Automatic multicarrier waveform classification via PCA and convolutional neural networks [J]. IEEE Access , 2018 , 6 : 51365 - 51373 .

WONG L J , HEADLEY W C , MICHAELS A J . Specific emitter identification using convolutional neural network-based IQ imbalance estimators [J]. IEEE Access , 2019 , 7 : 33544 - 33555 .

HUANG S , CHAI L , LI Z N , et al . Automatic modulation classification using compressive convolutional neural network [J]. IEEE Access , 2019 , 7 : 79636 - 79643 .

ZHANG X W , SEYFI T , JU S T , et al . Deep learning for interference identification: Band, training SNR, and sample selection [C]// 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications . Cannes : IEEE , 2019 : 1 - 5 .

LIANG F , SHEN C , YU W , et al . Towards optimal power control via ensembling deep neural networks [J]. IEEE Transactions on Communications , 2020 , 68 ( 3 ): 1760 - 1776 .

CEVIKALP H , TRIGGS B , FRANC V . Face and landmark detection by using cascade of classifiers [C]// 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition . Shanghai : IEEE , 2013 : 1 - 7 .

CEVIKALP H . Best fitting hyperplanes for classification [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1076 - 1088 .

DHAMIJA A R , GÜNTHER M , BOULT T E . Reducing Network Agnostophobia [EB/OL]. ( 2018 )[2021]. https://arxiv.org/abs/1811.04110 https://arxiv.org/abs/1811.04110 .

SHU L , XU H , LIU B . DOC: Deep Open Classification of Text Documents [EB/OL]. ( 2017 )[2021]. https://arxiv.org/abs/1709.08716 https://arxiv.org/abs/1709.08716 .

LIU Z W , MIAO Z Q , ZHAN X H , et al . Large-scale long-tailed recognition in an open world [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach : IEEE , 2019 : 2532 - 2541 .

HASSEN M , CHAN P K . Learning a neural-network-based representation for open set recognition [C]// Proceedings of the 2020 SIAM International Conference on Data Mining . Philadelphia, PA : Society for Industrial and Applied Mathematics , 2020 : 154 - 162 .

GE Z Y , DEMYANOV S , GARNAVI R . Generative OpenMax for multi-class open set classification [C]// Proceedings of the British Machine Vision Conference 2017 . London : British Machine Vision Association , 2017 : 42.1- 42 . 12 .

YU Y , QU W Y , LI N , et al . Open category classification by adversarial sample generation [C]// Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence . California : International Joint Conferences on Artificial Intelligence Organization , 2017 : 469 .

CHEN G Y , PENG P X , WANG X Q , et al . Adversarial reciprocal points learning for open set recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , DOI: 10.1109/TPAMI.2021.3106743 http://dx.doi.org/10.1109/TPAMI.2021.3106743 .

DE MAESSCHALCK R , JOUAN-RIMBAUD D , MASSART D L . The mahalanobis distance [J]. Chemometrics and Intelligent Laboratory Systems , 2000 , 50 ( 1 ): 1 - 18 .

WANG X , HAN X T , HUANG W L , et al . Multi-similarity loss with general pair weighting for deep metric learning [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach : IEEE , 2019 : 5017 - 5025 .

WEN Y D , ZHANG K P , LI Z F , et al . A discriminative feature learning approach for deep face recognition [C]// European Conference On Computer Vision . Cham : Springer , 2016 : 499 - 515 ..

MENSINK T , VERBEEK J , PERRONNIN F , et al . Distance-based image classification: Generalizing to new classes at near-zero cost [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013 , 35 ( 11 ): 2624 - 2637 .

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