论文标题
用子阵列采样的天线阵列的DOA估计和模型订单选择的机器学习方法
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling
论文作者
论文摘要
在本文中，我们研究了采用子阵列采样的系统的到达估计方向和模型订单选择的问题。因此，我们专注于场景，其中活动源的数量不小于同时采样天线元件的数量。为此，我们提出了基于神经网络和估计器的新方案，这些方案将神经网络与可能性功能的梯度步骤相结合。这些方法能够在平方误差和模型选择准确性方面胜过现有的估计器，尤其是在低快照域中，计算复杂性较大。
In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of active sources is not smaller than the number of simultaneously sampled antenna elements. For this purpose, we propose new schemes based on neural networks and estimators that combine neural networks with gradient steps on the likelihood function. These methods are able to outperform existing estimators in terms of mean squared error and model selection accuracy, especially in the low snapshot domain, at a drastically lower computational complexity.
