Scalable End-to-End RF Classification: A Case Study on Undersized Dataset Regularization by Convolutional-MST Article Swipe
Khalid Youssef
,
Greg Schuette
,
Yubin Cai
,
Daisong Zhang
,
Yikun Huang
,
Yahya Rahmat‐Samii
,
Louis‐S. Bouchard
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.12103
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.12103
Unlike areas such as computer vision and speech recognition where convolutional and recurrent neural networks-based approaches have proven effective to the nature of the respective areas of application, deep learning (DL) still lacks a general approach suitable for the unique nature and challenges of RF systems such as radar, signals intelligence, electronic warfare, and communications. Existing approaches face problems in robustness, consistency, efficiency, repeatability and scalability. One of the main challenges in RF sensing such as radar target identification is the difficulty and cost of obtaining data. Hundreds to thousands of samples per class are typically used when training for classifying signals into 2 to 12 classes with reported accuracy ranging from 87% to 99%, where accuracy generally decreases with more classes added. In this paper, we present a new DL approach based on multistage training and demonstrate it on RF sensing signal classification. We consistently achieve over 99% accuracy for up to 17 diverse classes using only 11 samples per class for training, yielding up to 35% improvement in accuracy over standard DL approaches.
Related Topics
Concepts
Scalability
Computer science
Robustness (evolution)
Convolutional neural network
Artificial intelligence
Regularization (linguistics)
End-to-end principle
Radar
Machine learning
Ranging
Pattern recognition (psychology)
Facial recognition system
Electronic warfare
Data mining
Telecommunications
Database
Biochemistry
Chemistry
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2104.12103
- https://arxiv.org/pdf/2104.12103
- OA Status
- green
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3157182036
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3157182036Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.12103Digital Object Identifier
- Title
-
Scalable End-to-End RF Classification: A Case Study on Undersized Dataset Regularization by Convolutional-MSTWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-04-25Full publication date if available
- Authors
-
Khalid Youssef, Greg Schuette, Yubin Cai, Daisong Zhang, Yikun Huang, Yahya Rahmat‐Samii, Louis‐S. BouchardList of authors in order
- Landing page
-
https://arxiv.org/abs/2104.12103Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2104.12103Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2104.12103Direct OA link when available
- Concepts
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Scalability, Computer science, Robustness (evolution), Convolutional neural network, Artificial intelligence, Regularization (linguistics), End-to-end principle, Radar, Machine learning, Ranging, Pattern recognition (psychology), Facial recognition system, Electronic warfare, Data mining, Telecommunications, Database, Biochemistry, Chemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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75Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2612573399, https://openalex.org/W2142889010, https://openalex.org/W1735714752, https://openalex.org/W2247062920, https://openalex.org/W2743923360, https://openalex.org/W1928560848, https://openalex.org/W2807981689, https://openalex.org/W1679913846, https://openalex.org/W2746870488, https://openalex.org/W2147228188, https://openalex.org/W2130549393, https://openalex.org/W1576278180, https://openalex.org/W2536305597, https://openalex.org/W2410591237, https://openalex.org/W2592680288, https://openalex.org/W2006544565, https://openalex.org/W2162509629, https://openalex.org/W1996208437, https://openalex.org/W2964121744, https://openalex.org/W1519794338, https://openalex.org/W2120821959, https://openalex.org/W1832808418, https://openalex.org/W2962970834, https://openalex.org/W2053190774, https://openalex.org/W2004915807, https://openalex.org/W1909324335, https://openalex.org/W2120695425, https://openalex.org/W2033831971, https://openalex.org/W2099471712, https://openalex.org/W2614504311, https://openalex.org/W1697183041, https://openalex.org/W1553521424, https://openalex.org/W2158565471, https://openalex.org/W2107647130, https://openalex.org/W2469690627, https://openalex.org/W2794189208, https://openalex.org/W2567126514, https://openalex.org/W576316702, https://openalex.org/W2152417180, https://openalex.org/W2995715529, https://openalex.org/W3129181224, https://openalex.org/W3083113686, https://openalex.org/W2100115174, https://openalex.org/W3008717905, https://openalex.org/W2141955292, https://openalex.org/W1819164142, https://openalex.org/W2987064925, https://openalex.org/W2617720975, https://openalex.org/W2025924350, https://openalex.org/W2108729336, https://openalex.org/W2102017903, https://openalex.org/W1978201669, https://openalex.org/W1548287226, https://openalex.org/W2133764509, https://openalex.org/W322998299, https://openalex.org/W2129426113, https://openalex.org/W2982486887, https://openalex.org/W1712206955, https://openalex.org/W2485761378, https://openalex.org/W2006258746, https://openalex.org/W1859791015, https://openalex.org/W1977142757, https://openalex.org/W1819869616, https://openalex.org/W2488544472, https://openalex.org/W1538131130, https://openalex.org/W2789436454, https://openalex.org/W2900037340, https://openalex.org/W2033154814, https://openalex.org/W2155220627, https://openalex.org/W2158412489, https://openalex.org/W2106520387, https://openalex.org/W2045656233, https://openalex.org/W3030916542, https://openalex.org/W1585739719, https://openalex.org/W1810146585 |
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