A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors

TL;DR

This paper introduces a deep learning method for estimating the direction of arrival using automotive ultrasonic sensors, outperforming traditional algorithms in noisy and real-world conditions.

Contribution

The paper presents a novel deep learning approach that surpasses existing deterministic algorithms for ultrasonic DOA estimation in automotive applications.

Findings

Deep learning approach outperforms traditional algorithms in noisy environments.

Proposed method reduces errors caused by environmental noise and measurement inaccuracies.

Approach overcomes limitations like triangulation precision loss and aliasing.

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.