Resolving Elevation Ambiguity in 1-D Radar Array Measurements using Deep Learning

• Published in: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 5883 - 5888
• Main Authors: Unnikrishnan, Jayakrishnan, Niesen, Urs
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• ISSN: 2153-0866
• DOI: 10.1109/IROS40897.2019.8967879

Motivated by requirements for future automotive radar, we study the problem of resolving target elevation from measurements by a one-dimensional horizontal radar antenna array. This is a challenging and ill-posed problem, since such measurements contain only indirect and highly ambiguous elevation cues. As a consequence, traditional model-based approaches fail. We instead propose to use a machine-learning-based approach that learns to exploit the subtle elevation cues and prior knowledge of the scene from the data. We design an encoder-decoder structured deep convolutional neural network that takes a radar return intensity image in the range-azimuth plane as input and produces a depth image in the elevation-azimuth 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.