Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern

TL;DR

This paper presents a deep learning-based method using a conditional Wasserstein GAN to synthesize MIMO radar waveforms with customizable beampatterns, enabling on-demand generation of interference-reducing signals with low computational cost.

Contribution

It introduces a novel deep generative model for flexible synthesis of MIMO waveforms with desired properties, extending previous orthogonal code methods.

Findings

Capable of synthesizing unique phase codes in real-time

Achieves desired beampatterns with lower computational complexity

Enhances interference reduction and LPI/LPD capabilities

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.