A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors
This addresses improved accuracy for driving assistance systems like automatic parking, though it appears incremental as it builds on known methods.
The paper tackles direction of arrival estimation for automotive ultrasonic sensors, proposing a deep learning approach that outperforms existing deterministic algorithms in simulations and real measurements under noisy conditions.
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.