Analog Beamforming for Active Imaging using Sparse Arrays

This paper studies analog beamforming in active sensing applications, such as millimeter-wave radar or ultrasound imaging. Analog beamforming architectures employ a single RF-IF front end connected to all array elements via inexpensive phase shifters. This can drastically lower costs compared to fully-digital beamformers having a dedicated front end for each sensor. However, controlling only the element phases may lead to elevated sidelobe levels and degraded image quality. We address this issue by image addition, which synthesizes a high resolution image by adding together several lower resolution component images. Image addition also facilitates the use of sparse arrays, which can further reduce array costs. To limit the image acquisition time, we formulate an optimization problem for minimizing the number of component images, subject to achieving a desired point spread function. We then propose a gradient descent algorithm for approximately solving this problem. We also derive an upper bound on the number of component images needed by the analog beamformer to achieve the conventional digital beamforming solution.