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Combined CS and DL techniques for DOA with a Rotman Lens

: Weiß, M.; Kohler, M.; Saam, A.; Worms, J.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Radar Conference, RadarConf 2020 : September 21-25, 2020, Florence, Italy, virtual event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-8943-7
ISBN: 978-1-7281-8942-0
5 S.
Radar Conference (RadarConf) <2020, Online>
Fraunhofer FHR ()

Rotman lenses are useful devices commonly utilized within multi-beam antenna array networks. They are generally used in radar surveillance systems to detect targets in multiple directions simultaneously without physically moving the antenna front-end. Nowadays, the communications sector (5G) also has great interest in this technology. Due to the use of a free-space true-time delay network, for instance attached to an Uniform Linear Array (ULA) consisting of broadband Vivaldi antenna elements, this type of microwave lens support wide-band operation with low-phase error estimation, and wide-angle scanning combined with simultaneous spatial beams (beamspace) for fast coverage. In particular the multi-beam feature makes the lens very attractive for Direction of Arrival (DoA) applications. This paper combines the aforementioned advantage with a dedicated Neural Network (NN) for an efficient wideband Direction of Arrival (DoA) and frequency estimation technique based on a single snapshot from such a multi-beam antenna configuration. The proposed approach uses machine learning techniques to establish the NN with a training set obtained from measurements in an anechoic chamber enriched superimposing different noise levels. This results in a lower computational load during the training phase and finally a very fast estimation of direction and frequency of the impinging signal.