GNSS interference and spoofing dataset

Wang, Xiaoyan; Yang, Jingjing; Huang, Ming; Peng, Zixiao

doi:10.1016/j.dib.2024.110302

Data in Brief

2024

DOI: 10.1016/j.dib.2024.110302

|View full text |Cite

GNSS interference and spoofing dataset

Xiaoyan Wang,

Jingjing Yang,

Ming Huang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

Zhao,

Han,

Chen

et al. 2024

Electronics

View full text Add to dashboard Cite

Deep learning has presented its spectacular potential in the jamming recognition field. Yet, sufficient samples required by normal deep learning analysis methods are not always available, especially in the 6G communication field. This situation appears to be more challenging in the communication field. In this article, Model-Agnostic Meta-Learning (MAML) is imported into the jamming recognition field in order to accomplish compound jamming recognition in the circumstances of few-shot learning. Further, the existing research on jamming recognition techniques is mostly based on single-node recognition. This technique cannot make full and efficient use of the jamming information collected. Therefore, this article adds a multi-node collaborative technique into the compound jamming recognition algorithm that is based on MAML and time-frequency analysis. Based on the fact that each cognitive node can recognize independently, the recognition results are be sent to the fusion center. The fusion center completes the fusion of the recognition results according to the majority rule. The experiments demonstrate that, with the fusion of the multi-node collaborative technique, the precision of compound jamming recognition in the condition of few-shot learning has been effectively improved.

show abstract

The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

Zhao,

Han,

Chen

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

GNSS interference and spoofing dataset

Cited by 1 publication

References 7 publications

The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

Contact Info

Product

Resources

About