Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM
The goal of radar emitter recognition (RER) is to extract the features of the received emitter signal. This has become a critical issue as new radar types are emerging, and the electromagnetic environment is becoming denser and more complex. Deep neural networks (DNNs) have recently proven effective...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9845424/ |
| _version_ | 1818504241498554368 |
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| author | Sidra Ghayour Bhatti Aamer Iqbal Bhatti |
| author_facet | Sidra Ghayour Bhatti Aamer Iqbal Bhatti |
| author_sort | Sidra Ghayour Bhatti |
| collection | DOAJ |
| description | The goal of radar emitter recognition (RER) is to extract the features of the received emitter signal. This has become a critical issue as new radar types are emerging, and the electromagnetic environment is becoming denser and more complex. Deep neural networks (DNNs) have recently proven effective for emitter identification; however, the recognition of phase-coded waveforms at a low signal to noise ratio (SNR) remains challenging. In this paper, a novel phase-based RER approach using short time fourier transform (STFT) and bidirectional long short term memory (BiLSTM) is proposed, while enhancing the ability to learn features from noisy signals. The phase spectrum of phase-coded signals was analyzed in contrast to the amplitude spectrum used in state-of-the-art approaches in the literature. The derived phase-based features were directly provided as inputs to the proposed BiLSTM architecture. The fully connected layer follows the BiLSTM layer. Finally, a softmax classifier was employed to accomplish the recognition task. Six distinct types of phase-coded waveforms degraded by additive white gaussian noise (AWGN) with SNRs ranging from −8 dB to 8 dB were simulated. The method proposed in this research involves simple pre-processing and exhibits an overall recognition accuracy of more than 90% at SNR of −2 dB. |
| first_indexed | 2024-12-10T21:34:31Z |
| format | Article |
| id | doaj.art-d1bfbe92fa7646bb8633c09e10c9a42a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T21:34:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1bfbe92fa7646bb8633c09e10c9a42a2022-12-22T01:32:41ZengIEEEIEEE Access2169-35362022-01-0110801848019410.1109/ACCESS.2022.31952739845424Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTMSidra Ghayour Bhatti0https://orcid.org/0000-0003-4094-5984Aamer Iqbal Bhatti1https://orcid.org/0000-0002-3373-3388Department of Electrical Engineering, Capital University of Science and Technology, Islamabad, PakistanDepartment of Electrical Engineering, Capital University of Science and Technology, Islamabad, PakistanThe goal of radar emitter recognition (RER) is to extract the features of the received emitter signal. This has become a critical issue as new radar types are emerging, and the electromagnetic environment is becoming denser and more complex. Deep neural networks (DNNs) have recently proven effective for emitter identification; however, the recognition of phase-coded waveforms at a low signal to noise ratio (SNR) remains challenging. In this paper, a novel phase-based RER approach using short time fourier transform (STFT) and bidirectional long short term memory (BiLSTM) is proposed, while enhancing the ability to learn features from noisy signals. The phase spectrum of phase-coded signals was analyzed in contrast to the amplitude spectrum used in state-of-the-art approaches in the literature. The derived phase-based features were directly provided as inputs to the proposed BiLSTM architecture. The fully connected layer follows the BiLSTM layer. Finally, a softmax classifier was employed to accomplish the recognition task. Six distinct types of phase-coded waveforms degraded by additive white gaussian noise (AWGN) with SNRs ranging from −8 dB to 8 dB were simulated. The method proposed in this research involves simple pre-processing and exhibits an overall recognition accuracy of more than 90% at SNR of −2 dB.https://ieeexplore.ieee.org/document/9845424/Bidirectional long short term memory (BiLSTM)emitter identificationphase coded waveformstime-frequency transformdeep neural network |
| spellingShingle | Sidra Ghayour Bhatti Aamer Iqbal Bhatti Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM IEEE Access Bidirectional long short term memory (BiLSTM) emitter identification phase coded waveforms time-frequency transform deep neural network |
| title | Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM |
| title_full | Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM |
| title_fullStr | Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM |
| title_full_unstemmed | Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM |
| title_short | Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM |
| title_sort | radar signals intrapulse modulation recognition using phase based stft and bilstm |
| topic | Bidirectional long short term memory (BiLSTM) emitter identification phase coded waveforms time-frequency transform deep neural network |
| url | https://ieeexplore.ieee.org/document/9845424/ |
| work_keys_str_mv | AT sidraghayourbhatti radarsignalsintrapulsemodulationrecognitionusingphasebasedstftandbilstm AT aameriqbalbhatti radarsignalsintrapulsemodulationrecognitionusingphasebasedstftandbilstm |