论文标题
SDOA-NET：不完美数组的有效基于深度学习的DOA估计网络
SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array
论文作者
论文摘要
到达方向（DOA）的估计是常规雷达，无线通信以及集成感应和通信（ISAC）系统的关键问题。但是，低成本系统通常会遭受不完善的因素，例如天线位置扰动，相互耦合效应，不一致的增益/相位以及非线性放大器效应，这会大大降低DOA估计的性能。本文提出了一种基于深度学习（DL）的DOA估计方法，称为超分辨率DOA网络（SDOA-NET），以更准确地表征现实的数组。与现有的基于DL的DOA方法不同，SDOA-NET使用采样的接收信号而不是协方差矩阵作为提取数据功能的输入。此外，SDOA-NET会产生独立于目标DOA的向量，但可用于估计其空间频谱。因此，可以将同一培训网络应用于任何数量的目标，从而降低实施的复杂性。与现有基于DL的方法相比，具有低维网络结构的SDOA-NET的收敛速度也更快。模拟结果表明，SDOA-NET优于不完美数组的现有DOA估计方法。 SDOA-NET代码可在https://github.com/chenpengseu/sdoa-net.git上在线获得。
The estimation of direction of arrival (DOA) is a crucial issue in conventional radar, wireless communication, and integrated sensing and communication (ISAC) systems. However, low-cost systems often suffer from imperfect factors, such as antenna position perturbations, mutual coupling effect, inconsistent gains/phases, and non-linear amplifier effect, which can significantly degrade the performance of DOA estimation. This paper proposes a DOA estimation method named super-resolution DOA network (SDOA-Net) based on deep learning (DL) to characterize the realistic array more accurately. Unlike existing DL-based DOA methods, SDOA-Net uses sampled received signals instead of covariance matrices as input to extract data features. Furthermore, SDOA-Net produces a vector that is independent of the DOA of the targets but can be used to estimate their spatial spectrum. Consequently, the same training network can be applied to any number of targets, reducing the complexity of implementation. The proposed SDOA-Net with a low-dimension network structure also converges faster than existing DL-based methods. The simulation results demonstrate that SDOA-Net outperforms existing DOA estimation methods for imperfect arrays. The SDOA-Net code is available online at https://github.com/chenpengseu/SDOA-Net.git.
