Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments
Accurately classifying and deinterleaving overlapping radar signals presents a significant challenge in complex environments, such as electronic warfare. Traditional methods, such as spectrogram-based analysis, often struggle to differentiate radar signals with similar scan patterns, particularly un...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7612 |
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| author | Ji-Hyeon Kim Soon-Young Kwon Hyoung-Nam Kim |
| author_facet | Ji-Hyeon Kim Soon-Young Kwon Hyoung-Nam Kim |
| author_sort | Ji-Hyeon Kim |
| collection | DOAJ |
| description | Accurately classifying and deinterleaving overlapping radar signals presents a significant challenge in complex environments, such as electronic warfare. Traditional methods, such as spectrogram-based analysis, often struggle to differentiate radar signals with similar scan patterns, particularly under low signal-to-noise ratio (SNR) conditions. To address these limitations, we propose a novel two-stage classification framework that combines amplitude pattern (AMP) analysis and visibility graphs to enhance the accuracy and efficiency of radar signal classification. In the first stage, AMP analysis groups radar reception signals into broad categories, which reduces noise and isolates signal features. In the second stage, a visibility graph technique is applied to refine these classifications, enabling the practical separation of radar signals with overlapping or similar amplitude features. The proposed method is particularly effective in handling complex scans, such as the Palmer series, which blends search and tracking patterns. Deep learning models, including GoogLeNet and ResNet, are integrated within this framework to improve classification performance further, demonstrating robustness in low-SNR and multi-signal environments. This approach offers significant improvements over conventional methods, providing enhanced performance in differentiating radar signals across various scanning patterns in challenging multi-signal environments. |
| format | Article |
| id | doaj-art-932d7d4985dc4a8e94cca50fdec80edd |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-932d7d4985dc4a8e94cca50fdec80edd2025-08-20T01:55:45ZengMDPI AGSensors1424-82202024-11-012423761210.3390/s24237612Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal EnvironmentsJi-Hyeon Kim0Soon-Young Kwon1Hyoung-Nam Kim2Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaAccurately classifying and deinterleaving overlapping radar signals presents a significant challenge in complex environments, such as electronic warfare. Traditional methods, such as spectrogram-based analysis, often struggle to differentiate radar signals with similar scan patterns, particularly under low signal-to-noise ratio (SNR) conditions. To address these limitations, we propose a novel two-stage classification framework that combines amplitude pattern (AMP) analysis and visibility graphs to enhance the accuracy and efficiency of radar signal classification. In the first stage, AMP analysis groups radar reception signals into broad categories, which reduces noise and isolates signal features. In the second stage, a visibility graph technique is applied to refine these classifications, enabling the practical separation of radar signals with overlapping or similar amplitude features. The proposed method is particularly effective in handling complex scans, such as the Palmer series, which blends search and tracking patterns. Deep learning models, including GoogLeNet and ResNet, are integrated within this framework to improve classification performance further, demonstrating robustness in low-SNR and multi-signal environments. This approach offers significant improvements over conventional methods, providing enhanced performance in differentiating radar signals across various scanning patterns in challenging multi-signal environments.https://www.mdpi.com/1424-8220/24/23/7612radar scan pattern classificationamplitude patternvisibility graph |
| spellingShingle | Ji-Hyeon Kim Soon-Young Kwon Hyoung-Nam Kim Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments Sensors radar scan pattern classification amplitude pattern visibility graph |
| title | Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments |
| title_full | Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments |
| title_fullStr | Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments |
| title_full_unstemmed | Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments |
| title_short | Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments |
| title_sort | enhanced radar signal classification using amp and visibility graph for multi signal environments |
| topic | radar scan pattern classification amplitude pattern visibility graph |
| url | https://www.mdpi.com/1424-8220/24/23/7612 |
| work_keys_str_mv | AT jihyeonkim enhancedradarsignalclassificationusingampandvisibilitygraphformultisignalenvironments AT soonyoungkwon enhancedradarsignalclassificationusingampandvisibilitygraphformultisignalenvironments AT hyoungnamkim enhancedradarsignalclassificationusingampandvisibilitygraphformultisignalenvironments |