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arXiv · 2412.20883

Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern

Vesa Saarinen·Robin Rajamäki·Visa Koivunen
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Abstract

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.

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Vesa Saarinen, Robin Rajamäki, Visa Koivunen. 2024-12-30. Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern. https://arxiv.org/abs/2412.20883

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