极稀疏共素感知：低延迟估计不是梦想而是现实
Extremely Sparse Co-Prime Sensing: Low Latency Estimation is not a Dream but a Reality
摘要：
互质检测是一种用于信号采集的亚奈奎斯特技术。在文献中已经提出了对原型互质阵列的几种修改。研究人员还证明了低延迟估计。本文介绍了一个家庭的可调枢轴共素阵列的功能。然而，主要的重点是向读者介绍一个称为极稀疏共素阵列和采样器的概念。可调主元互质阵列是极稀疏阵列的一种特殊情况。推导了权函数和相关图偏置窗口的闭合表达式。低延迟估计证明使用极其稀疏的互质方案。此外，一个多维和一个混合极稀疏互质阵列提出了作为一个简单的扩展的一维理论。最后，一个广义的极稀疏的结构开发了几个设计参数。大多数现有的结构可以被看作是一个特殊的情况下，广义计划。
英文摘要：
Co-prime sensing is a sub-Nyquist technique for signal acquisition. Several modifications to the prototype co-prime array have been proposed in the literature. Researchers have also demonstrated low latency estimation. This paper describes the functioning of a Family of Adjustable Pivot Co-Prime Arrays. However, the main focus is to introduce the reader to a concept called Extremely Sparse Co-Prime Arrays and Samplers. Adjustable pivot co-prime arrays are a special case of the extremely sparse arrays. The closed-form expressions for the weight function and the correlogram bias window are derived. Low latency estimation is demonstrated using the extremely sparse co-prime scheme. Furthermore, a multidimensional and a hybrid extremely sparse co-prime array is proposed as a straightforward extension of the 1D-theory. Finally, a generalized extremely sparse structure is developed with several design parameters. Most existing structures may be viewed as a special case of the generalized scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
