基于深度学习的汽车级超声波传感器波达方向估计方法
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors
摘要：
本文提出了一种深度学习方法，用于使用汽车级超声波传感器进行波达方向估计，该传感器用于自动泊车等驾驶辅助系统。最先进的确定性波达方向估计算法的研究和实现被用作所提出的方法的性能的基准。对现有算法的性能进行了分析所提出的算法的仿真数据，以及从使用汽车级超声波传感器完成的测量活动的数据。这两组结果清楚地表明，所提出的方法在现实条件下的优越性，如来自环境的噪声以及最终的测量误差。它也证明了所提出的方法可以克服现有算法的一些已知的局限性，如精度稀释的三角剖分和混叠。
英文摘要：
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.
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