Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization. Recently, machine learning is being leveraged for obtaining attractive solutions to more complex beamforming problems. This article captures the evolution of beamforming in the last twenty-five years from convex-to-nonconvex optimization and optimization-to-learning approaches. It provides a glimpse of this important signal processing technique into a variety of transmit-receive architectures, propagation zones, paths, and conventional/emerging applications.
翻译:光束成像是一种引导、形状和聚焦电磁波的信号处理技术,它使用一系列传感器引导、形状和聚焦电磁波,向一个理想的方向发展。它被用于雷达、声纳、声响、天文学、地震学、医学成像和通信等若干工程应用。由于多电网技术的进展,主要用于雷达和通信,人们对光束设计非常感兴趣,主要依靠对流/非电流的优化。最近,正在利用机器学习获得对更复杂的波形问题的有吸引力的解决方案。这篇文章捕捉了过去25年中波束的演进,从连接至非电流优化和优化到学习的方法。它为这一重要的信号处理技术提供了一瞥,将它转化为各种传输-接收结构、传播区、路径和常规/生成应用。