Radars improve the sensing robustness of unmanned aerial vehicles (UAVs) by operating under poor lighting and weather conditions and seeing through occlusions, such as vegetation. However, they suffer from poor angular resolution, which can be addressed using synthetic aperture radar (SAR) algorithms. The state-of-the-art UAV SAR methods operate at a depression angle and are not suitable for sensor fusion applications, where the data are collected from areas directly below the UAV (i.e., the UAV nadir). In this article, we present an interferometric SAR (InSAR) framework for reconstructing 3D images from the UAV nadir using a low-cost multi-input-multi-output (MIMO) mm-wave radar. Additionally, an effective method based on the phase gradient autofocus (PGA) is presented for compensating the phase error across the virtual receive antennas. We demonstrate the effectiveness of our 3D imaging algorithm in both simulation and experimental scenarios.
