Design of deep learning based pulse repetition interval modulation classification and recognition
In radar signal processing area, pulse repetition interval (PRI) is a significant parameter, representing the time interval between consecutive radar pulse emissions. This is an important temporal property in identifying emitters and their modes of operation in electronic warfare. The emergence of d...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172920 |
| _version_ | 1826111653142331392 |
|---|---|
| author | Zhuang, Yiran |
| author2 | Teh Kah Chan |
| author_facet | Teh Kah Chan Zhuang, Yiran |
| author_sort | Zhuang, Yiran |
| collection | NTU |
| description | In radar signal processing area, pulse repetition interval (PRI) is a significant parameter, representing the time interval between consecutive radar pulse emissions. This is an important temporal property in identifying emitters and their modes of operation in electronic warfare. The emergence of deep learning has emerged the improvement of radar signal classification. This dissertation project focuses on the development and evaluation of an innovative deep learning-based model for automatic identification of multiple PRI modulation modes. This research is implemented using MATLAB and Python. The overall goal of this work is to advance PRI modulation identification techniques and contribute to the growth of knowledge on radar signal processing, with potential applications spanning all areas of radar technology. This project successfully developed a deep learning-based model for automatically identifying multiple PRI modulation modes in radar signals using MATLAB and Python. The model showed promise in improving PRI modulation recognition accuracy and efficiency. While the findings contribute to radar signal processing, it is important to acknowledge that further research and real-world validation may be needed to fully assess its practical impact.
Keywords: Pulse repetition interval, deep learning, convolutional neural network. |
| first_indexed | 2024-10-01T02:54:19Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/172920 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:54:19Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1729202024-01-12T15:44:52Z Design of deep learning based pulse repetition interval modulation classification and recognition Zhuang, Yiran Teh Kah Chan School of Electrical and Electronic Engineering EKCTeh@ntu.edu.sg Engineering::Electrical and electronic engineering In radar signal processing area, pulse repetition interval (PRI) is a significant parameter, representing the time interval between consecutive radar pulse emissions. This is an important temporal property in identifying emitters and their modes of operation in electronic warfare. The emergence of deep learning has emerged the improvement of radar signal classification. This dissertation project focuses on the development and evaluation of an innovative deep learning-based model for automatic identification of multiple PRI modulation modes. This research is implemented using MATLAB and Python. The overall goal of this work is to advance PRI modulation identification techniques and contribute to the growth of knowledge on radar signal processing, with potential applications spanning all areas of radar technology. This project successfully developed a deep learning-based model for automatically identifying multiple PRI modulation modes in radar signals using MATLAB and Python. The model showed promise in improving PRI modulation recognition accuracy and efficiency. While the findings contribute to radar signal processing, it is important to acknowledge that further research and real-world validation may be needed to fully assess its practical impact. Keywords: Pulse repetition interval, deep learning, convolutional neural network. Master of Science (Communications Engineering) 2024-01-07T12:17:44Z 2024-01-07T12:17:44Z 2023 Thesis-Master by Coursework Zhuang, Y. (2023). Design of deep learning based pulse repetition interval modulation classification and recognition. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172920 https://hdl.handle.net/10356/172920 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Zhuang, Yiran Design of deep learning based pulse repetition interval modulation classification and recognition |
| title | Design of deep learning based pulse repetition interval modulation classification and recognition |
| title_full | Design of deep learning based pulse repetition interval modulation classification and recognition |
| title_fullStr | Design of deep learning based pulse repetition interval modulation classification and recognition |
| title_full_unstemmed | Design of deep learning based pulse repetition interval modulation classification and recognition |
| title_short | Design of deep learning based pulse repetition interval modulation classification and recognition |
| title_sort | design of deep learning based pulse repetition interval modulation classification and recognition |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/172920 |
| work_keys_str_mv | AT zhuangyiran designofdeeplearningbasedpulserepetitionintervalmodulationclassificationandrecognition |