Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909444170842112 |
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| author | Saarinen, Vesa Rajamäki, Robin Koivunen, Visa |
| author_facet | Saarinen, Vesa Rajamäki, Robin Koivunen, Visa |
| contents | This paper develops a generative deep learning model for the synthesis of multiple-input multiple-output (MIMO) active sensing waveforms with desired properties, including constant modulus and a user-defined beampattern. The proposed approach is capable synthesizing unique phase codes of on-the-fly, which has the potential to reduce interference between co-existing active sensing systems and facilitate Low Probability of Intercept/Low Probability of Detection (LPI/LPD) radar operation. The paper extends our earlier work on synthesis of approximately orthogonal MIMO phase codes by introducing flexible control over the transmit beampatterns. The developed machine learning method employs a conditional Wasserstein Generative Adversarial Network (GAN) structure. The main benefits of the method are its ability to discover new waveforms on-demand (post training) and generate demanding beampatterns at lower computational complexity compared to structured optimization approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_20883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern Saarinen, Vesa Rajamäki, Robin Koivunen, Visa Signal Processing This paper develops a generative deep learning model for the synthesis of multiple-input multiple-output (MIMO) active sensing waveforms with desired properties, including constant modulus and a user-defined beampattern. The proposed approach is capable synthesizing unique phase codes of on-the-fly, which has the potential to reduce interference between co-existing active sensing systems and facilitate Low Probability of Intercept/Low Probability of Detection (LPI/LPD) radar operation. The paper extends our earlier work on synthesis of approximately orthogonal MIMO phase codes by introducing flexible control over the transmit beampatterns. The developed machine learning method employs a conditional Wasserstein Generative Adversarial Network (GAN) structure. The main benefits of the method are its ability to discover new waveforms on-demand (post training) and generate demanding beampatterns at lower computational complexity compared to structured optimization approaches. |
| title | Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.20883 |