论文标题
使用深神经网络的矢量传感器的到达估计方向
Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks
论文作者
论文摘要
矢量传感器是一种具有六个共处天线的传感器阵列，用于测量入射波的所有电磁场成分，在估计入射源的到达角度和极化方面具有优势。虽然对线性阵列的机器学习的角度估计进行了充分的研究，但对于向量传感器来说，没有类似的解决方案。在本文中，我们提出了神经网络，以根据从接收到的数据中提取的协方差矩阵来确定来源的数量并估算每个源的到达角度。此外，我们还提供了将输出角度与相应源匹配的解决方案，并使用此方法检查了错误分布。结果表明，神经网络可以通过多达5个来源实现合理准确的估计，尤其是在视野有限的情况下。
A vector sensor, a type of sensor array with six collocated antennas to measure all electromagnetic field components of incident waves, has been shown to be advantageous in estimating the angle of arrival and polarization of the incident sources. While angle estimation with machine learning for linear arrays has been well studied, there has not been a similar solution for the vector sensor. In this paper, we propose neural networks to determine the number of the sources and estimate the angle of arrival of each source, based on the covariance matrix extracted from received data. Also, we provide a solution for matching output angles to corresponding sources and examine the error distributions with this method. The results show that neural networks can achieve reasonably accurate estimation with up to 5 sources, especially if the field-of-view is limited.
