LPI Radar Waveform Recognition Based on Time-Frequency Distribution
In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals wid...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/10/1682 |
| _version_ | 1798026750546935808 |
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| author | Ming Zhang Lutao Liu Ming Diao |
| author_facet | Ming Zhang Lutao Liu Ming Diao |
| author_sort | Ming Zhang |
| collection | DOAJ |
| description | In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB. |
| first_indexed | 2024-04-11T18:41:51Z |
| format | Article |
| id | doaj.art-3bf409ccf0a54be8a347f96d1d95b3f8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:41:51Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3bf409ccf0a54be8a347f96d1d95b3f82022-12-22T04:08:58ZengMDPI AGSensors1424-82202016-10-011610168210.3390/s16101682s16101682LPI Radar Waveform Recognition Based on Time-Frequency DistributionMing Zhang0Lutao Liu1Ming Diao2College of Information and Telecommunication, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Telecommunication, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Telecommunication, Harbin Engineering University, Harbin 150001, ChinaIn this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.http://www.mdpi.com/1424-8220/16/10/1682LPI radartime-frequency distributiondigital image processingwaveform recognition |
| spellingShingle | Ming Zhang Lutao Liu Ming Diao LPI Radar Waveform Recognition Based on Time-Frequency Distribution Sensors LPI radar time-frequency distribution digital image processing waveform recognition |
| title | LPI Radar Waveform Recognition Based on Time-Frequency Distribution |
| title_full | LPI Radar Waveform Recognition Based on Time-Frequency Distribution |
| title_fullStr | LPI Radar Waveform Recognition Based on Time-Frequency Distribution |
| title_full_unstemmed | LPI Radar Waveform Recognition Based on Time-Frequency Distribution |
| title_short | LPI Radar Waveform Recognition Based on Time-Frequency Distribution |
| title_sort | lpi radar waveform recognition based on time frequency distribution |
| topic | LPI radar time-frequency distribution digital image processing waveform recognition |
| url | http://www.mdpi.com/1424-8220/16/10/1682 |
| work_keys_str_mv | AT mingzhang lpiradarwaveformrecognitionbasedontimefrequencydistribution AT lutaoliu lpiradarwaveformrecognitionbasedontimefrequencydistribution AT mingdiao lpiradarwaveformrecognitionbasedontimefrequencydistribution |