MIMO Radar Waveform Design for Multipath Exploitation Using Deep Learning

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 11; p. 2747
• Main Authors: Zheng, Zixiang, Zhang, Yue, Peng, Xiangyu, Xie, Hanfeng, Chen, Jinfan, Mo, Junxian, Sui, Yunfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2023
• Subjects: Algorithms; Antennas; Artificial intelligence; Comparative analysis; Convexity; Deep learning; Design; Design and construction; Design optimization; Design techniques; Exploitation; Methods; MIMO communication; MIMO radar; multipath exploitation; Neural networks; Optimization; Parameter estimation; Radar; Radar equipment; Radar systems; Receivers & amplifiers; Remote sensing; Signal processing; SINR; system optimization; Target detection; Technology application; waveform design; Waveforms; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15112747

This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver.