A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Elamir, Mohamed Shawki; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick
• Is Part Of: Journal of physics. Volume 2234:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: ultrasonic -- deep learning -- direction of arrival -- triangulation; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2234/1/012009
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking. A study and implementation of the state of the art deterministic direction of arrival estimation algorithms is used as a benchmark for the performance of the proposed approach. Analysis of the performance of the proposed algorithms against the existing algorithms is carried out over simulation data as well as data from a measurement campaign done using automotive-grade ultrasonic sensors. Both sets of results clearly show the superiority of the proposed approach under realistic conditions such as noise from the environment as well as eventual errors in measurements. It is demonstrated as well how the proposed approach can overcome some of the known limitations of the existing algorithms such as precision dilution of triangulation and aliasing.