论文标题
使用不完美的天线阵列对连贯源的DOA估算的深度自动编码器
Deep Autoencoders for DOA Estimation of Coherent Sources using Imperfect Antenna Array
论文作者
论文摘要
在本文中，提出了在存在天线阵列缺陷的情况下对相干来源的DOA估计的强大算法。我们利用当前的深度学习进步来克服最先进的DOA算法面临的最常见问题（即相干来源和阵列瑕疵）。我们提出了一个深层自动编码器（AE），该编码器能够正确解决相干来源而无需空间平滑，因此避免了可能的处理开销和延误。此外，我们假设接收的信号模型中存在阵列缺陷，例如相互耦合，增益/相位不匹配和位置误差。使用接收信号的协方差矩阵对深度AE进行了训练，该矩阵减轻了缺陷的影响，同时作为相干来源的过滤器。与文献中使用的方法相比，结果显示出显着改善。
In this paper a robust algorithm for DOA estimation of coherent sources in presence of antenna array imperfections is presented. We exploit the current advances of deep learning to overcome two of the most common problems facing the state of the art DOA algorithms (i.e. coherent sources and array imperfections). We propose a deep auto encoder (AE) that is able to correctly resolve coherent sources without the need of spatial smoothing, hence avoiding possible processing overhead and delays. Moreover, we assumed the presence of array imperfections in the received signal model such as mutual coupling, gain/ phase mismatches, and position errors. The deep AE is trained using the covariance matrix of the received signal, where it alleviates the effect of imperfections, and at the same time act as a filters for the coherent sources. The results show significant improvement compared to the methods used in the literature.
