Estimation and Removal of Strong Range Ambiguities in Multistatic Synthetic Aperture Radar With Multiple Elevation Beams Bistatic and multistatic synthetic aperture radar (SAR) can greatly improve system performance in various aspects, especially in achieving highresolution wideswath (HRWS) images. However, the specific system structure may lead to a higher degree of range ambiguities. This letter studies the structure of multistatic multiple elevation beams (MMEB) system which achieves HRWS images with the drawback of more serious range ambiguities compared with the conventional SAR which must be suppressed to get an acceptable system performance. The character that the receivers form, a prospective distributed multichannel system in azimuth, can give a guide in estimating the strong range ambiguities and remove them. An innovative method in estimating and removing (not just smearing) the strong range ambiguities based on this character is proposed. Theoretical analysis and experimental results show the effectiveness of the method. The proposed method considerably improves the range ambiguous performance of the MMEB system without using digital beamforming or pulse coding and makes full use of the system character, and thus improves the applicability of the system.

Resolving Elevation Ambiguity in 1D Radar Array Measurements using Deep Learning Motivated by requirements for future automotive radar, we study the problem of resolving target elevation from measurements by a onedimensional horizontal radar antenna array. This is a challenging and illposed problem, since such measurements contain only indirect and highly ambiguous elevation cues. As a consequence, traditional modelbased approaches fail. We instead propose to use a machinelearningbased approach that learns to exploit the subtle elevation cues and prior knowledge of the scene from the data. We design an encoderdecoder structured deep convolutional neural network that takes a radar return intensity image in the rangeazimuth plane as input and produces a depth image in the elevationazimuth plane as output. We train the network with over 200 000 radar frames collected in highway environments. Through experimental evaluations, we demonstrate the feasibility of resolving the highly ambiguous elevation information in such environments.