Learning Representations for Neural Networks Applied to Spectrum-Based Direction-of-Arrival Estimation for Automotive Radar

Gall M, Gardill M, Fuchs J, Horn T (2020)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Future Publication Type: Conference contribution

Publication year: 2020

Publisher: IEEE

Event location: Los Angeles, CA US

DOI: 10.1109/IMS30576.2020.9223841

Abstract

This paper proposes a new approach to Direction-of-Arrival Estimation using Artificial Neural Networks. It is capable of estimating both, model-order and azimuth DoA in a single step. In a hybrid approach, we train on synthetic data generated from a signal model and validate on data obtained through a measurement setup. We show a proof-of-concept for the hybrid approach, validated with measurement data. Advances on the exactness of the signal model enable the trained ANN to handle real-world data out-of-the-box. Our findings indicate super-resolution performance and the capability of estimating even high model-orders while significantly reducing computation time.

Authors with CRIS profile

Jonas Fuchs Lehrstuhl für Technische Elektronik

Involved external institutions

InnoSenT GmbH DE Germany (DE)

How to cite

APA:

Gall, M., Gardill, M., Fuchs, J., & Horn, T. (2020). Learning Representations for Neural Networks Applied to Spectrum-Based Direction-of-Arrival Estimation for Automotive Radar. In IEEE (Eds.), Proceedings of the 2020 IEEE MTT-S International Microwave Symposium (IMS). Los Angeles, CA, US: IEEE.

MLA:

Gall, Maximilian, et al. "Learning Representations for Neural Networks Applied to Spectrum-Based Direction-of-Arrival Estimation for Automotive Radar." Proceedings of the 2020 IEEE MTT-S International Microwave Symposium (IMS), Los Angeles, CA Ed. IEEE, IEEE, 2020.

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