Waveform Design of Carrier-Free UWB Sensors Based on Deep Learning Network

The development of radar sensors toward intelligent and adaptive is a major trend in the future. Adaptive waveform design is an important part to improve the performance of the sensor. This article investigates the signal waveform design problem of carrier-free ultra-wideband (UWB) radar sensors. The problem requires designing waveforms to detect and estimate extended targets in the presence of interference and clutter while imposing practical constraints on the designed signals, such as constant modulus or low peak-to-average power ratio (PAR). Since the resulting problem is high-dimensional and nonconvex (also known as NP-hard), finding the globally optimal solution through polynomial time algorithms is extremely challenging. We notice that deep convolutional neural networks (CNNs) are fundamentally nonlinear systems, making them well-suited for addressing the aforementioned problem. To this end, we propose a deep learning-based waveform design approach. This method is based on improved deep residual networks, utilizing the optimization problem construction in waveform optimization to form the loss function, and combining the Adam algorithm and the loss function to drive the optimization of deep residual networks. By leveraging the capability of deep convolutional networks to solve nonconvex problems, we achieve fast adaptive design of UWB sensor waveforms. Numerical examples are provided to validate the effectiveness of this approach.