用深度学习提升单快照下波达方向估计的分辨率。
(SP)$^2$-Net: A Neural Spatial Spectrum Method for DOA Estimation
- 设计新网络架构与训练策略，从单快照生成高分辨率空间谱。
- 在相同条件下，定位精度显著优于巴特利特波束成形器。
- 适合传感器阵列有限、需单次采样高精度测向的场景。
针对单快照下多源波达方向（DOA）估计问题，传统巴特利特波束成形器受限于阵列孔径，分辨率低。本文提出 (SP)²-Net，一种基于深度学习的空间谱生成方法：网络以阵列测量值和假设角度为输入，输出对应于更宽阵列性能的评分。推理时通过扫描任意角度生成热图。实验表明，该模型在精度和分辨率上均优于经典巴特利特波束成形器及基于稀疏性的方法，尤其适用于源数未知或数量大的单快照场景。
原文摘要 · Abstract (English)
We consider the problem of estimating the directions of arrival (DOAs) of multiple sources from a single snapshot of an antenna array, a task with many practical applications. In such settings, the classical Bartlett beamformer is commonly used, as maximum likelihood estimation becomes impractical when the number of sources is unknown or large, and spectral methods based on the sample covariance are not applicable due to the lack of multiple snapshots. However, the accuracy and resolution of the Bartlett beamformer are fundamentally limited by the array aperture. In this paper, we propose a deep learning technique, comprising a novel architecture and training strategy, for generating a high-resolution spatial spectrum from a single snapshot. Specifically, we train a deep neural network that takes the measurements and a hypothesis angle as input and learns to output a score consistent with the capabilities of a much wider array. At inference time, a heatmap can be produced by scanning an arbitrary set of angles. We demonstrate the advantages of our trained model, named (SP)$^2$-Net, over the Bartlett beamformer and sparsity-based DOA estimation methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。