LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function
In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8564 |
| _version_ | 1827719880222179328 |
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| author | Do-Hyun Park Min-Wook Jeon Da-Min Shin Hyoung-Nam Kim |
| author_facet | Do-Hyun Park Min-Wook Jeon Da-Min Shin Hyoung-Nam Kim |
| author_sort | Do-Hyun Park |
| collection | DOAJ |
| description | In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models. |
| first_indexed | 2024-03-10T20:54:57Z |
| format | Article |
| id | doaj.art-a704d0c470d4413c90e5312938b7068a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:57Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a704d0c470d4413c90e5312938b7068a2023-11-19T18:04:50ZengMDPI AGSensors1424-82202023-10-012320856410.3390/s23208564LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation FunctionDo-Hyun Park0Min-Wook Jeon1Da-Min Shin2Hyoung-Nam Kim3Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaIn electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.https://www.mdpi.com/1424-8220/23/20/8564electronic warfarelow-probability-of-interceptsignal detectiondeep learningtime-series analysis |
| spellingShingle | Do-Hyun Park Min-Wook Jeon Da-Min Shin Hyoung-Nam Kim LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function Sensors electronic warfare low-probability-of-intercept signal detection deep learning time-series analysis |
| title | LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function |
| title_full | LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function |
| title_fullStr | LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function |
| title_full_unstemmed | LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function |
| title_short | LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function |
| title_sort | lpi radar detection based on deep learning approach with periodic autocorrelation function |
| topic | electronic warfare low-probability-of-intercept signal detection deep learning time-series analysis |
| url | https://www.mdpi.com/1424-8220/23/20/8564 |
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