Concept analysis of a frequency-sweeping delta/sigma beam-switching radar using machine learning

This paper investigates a novel radar concept that is based on a minimalistic, small-aperture antenna array but features intelligent beam-shape switching and artificial-intelligence signal processing. In contrast to conventional phased-arrays, size, cost, and hardware complexity are drastically reduced by the proposed dual-antenna array which can create a broad and a frequency-scanning notched beam shape. The angular-resolution and target discrimination performance of the proposed radar concept have been validated by radar simulations for single and multiple target scenarios. For the signal processing, two convolutional neural networks (CNN) and a multilayer perceptron model are benchmarked against each other. A further CNN is implemented for estimating the number of targets, which can be used to pre-select the type of network determining range and cross-range of multiple targets. This paper shows that a small antenna aperture frontend in combination with beam-shape switching and artificial-intelligence signal processing methods is a suitable hardware-efficient radar concept for accurate multi-target location.