研究人员开发了用于到达方向（DoA）估计的新框架，这对于自主系统至关重要。这些方法利用Hankel结构感知和矩阵分解，在L2（高斯噪声）和L1（拉普拉斯噪声）范数下均提供最佳性能。所提出的技术展示了增强的超分辨率能力，与现有方法相比，所需的信噪比更低，并实现了更高的分辨率概率，已通过模拟和真实世界实验得到验证。 AI
影响 DoA估计的进步可以改善自主系统中的传感器融合和环境感知。
排序理由 两篇arXiv论文详细介绍了使用矩阵分解进行到达方向估计的新算法。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2604.26787v1 Announce Type: new Abstract: We consider the problems of computing the optimal rank-$1$ Hankel and Toeplitz-structured approximation of arbitrary matrices under $L_2$ and $L_1$-norm error. Such problems arise naturally in engineered systems, including the basic…
arXiv:2604.26793v1 Announce Type: new Abstract: Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence time, we develop a novel framework for rapid super-resolution multi-signal dire…
Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence time, we develop a novel framework for rapid super-resolution multi-signal direction-of-arrival (DoA) estimation based on Hanke…
We consider the problems of computing the optimal rank-$1$ Hankel and Toeplitz-structured approximation of arbitrary matrices under $L_2$ and $L_1$-norm error. Such problems arise naturally in engineered systems, including the basic few-shot signal Direction-of-Arrival (DoA) esti…