Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning's success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.

翻译：稀疏线性阵列中的单快拍信号处理技术日益重要，尤其在汽车雷达系统等动态环境中，可获取的快拍数量通常有限。这类阵列常为降低制造成本而设计，或由天线意外故障导致，由此带来高旁瓣电平、波达方向（DOA）估计精度下降等挑战。尽管深度学习在DOA估计等任务中已取得显著成果，但为增加目标数量或提升角度分辨率所需的大量训练数据仍构成严峻挑战。为此，本文提出一种新型孪生神经网络（SNN），其配备稀疏增强层，可有效提升稀疏阵列中的信号特征嵌入能力与DOA估计精度。通过细致的特征分析与性能评估，我们验证了该方法在DOA估计性能上的显著提升。本研究代码公开于 https://github.com/ruxinzh/SNNS_SLA。