论文标题
使用汽车级超声传感器的深度学习方法来估算到达方向
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors
论文作者
论文摘要
在本文中，提出了一种深度学习方法，用于使用汽车级超声传感器的到达估算方向，该传感器用于驾驶辅助系统，例如自动停车。最先进的到达估计算法确定性方向的研究和实施用作拟议方法的性能的基准。对现有算法的算法的性能进行分析，是对模拟数据以及使用汽车级超声传感器进行的测量活动进行的数据进行的。两组结果清楚地表明了在现实条件下（例如来自环境的噪声）以及测量中最终误差的拟议方法的优越性。还证明了所提出的方法如何克服现有算法的某些已知局限性，例如精确稀释三角测量和混叠。
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.
