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Accurate Deep CNN-Based Waveform Recognition for Intelligent Radar Systems
- Source :
- IEEE Communications Letters. 25:2938-2942
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically challenged by practical problems of scalability and reliability. Deep learning (DL) with the ability to automatically learn the representational features is leveraged to handle the aforementioned obstacles effectively. In this work, we proposed a high-accurate waveform recognition method for intelligent radar systems by developing a novel residual-attention multiscale-accumulation convolutional network (RamNet). By deliberately incorporating the residual connection and attention connection in selective-feature improvement blocks, RamNet can enrich high-impact features without vanishing gradient. Moreover, a structural multiscale-accumulation connection is deployed to improve feature utilization by gathering the high-relevant features at multiple signal resolutions. Experimental results from exhaustive simulation demonstrate that RamNet recognizes waveform robustly under rigorous channel impairments and presents superior performance compared to traditional ML and state-of-the-art DL models.
- Subjects :
- Feature engineering
business.industry
Computer science
Deep learning
Feature extraction
Communications system
Computer Science Applications
law.invention
Computer engineering
law
Modeling and Simulation
Scalability
Feature (machine learning)
Waveform
Artificial intelligence
Electrical and Electronic Engineering
Radar
business
Subjects
Details
- ISSN :
- 23737891 and 10897798
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- IEEE Communications Letters
- Accession number :
- edsair.doi...........a013f3a8574995951cfd7b03b7872d56