论文标题
超级共同传感
Super-Nyquist Co-Prime Sensing
论文作者
论文摘要
过去已经研究了联合阵列的理论。使用较低的延迟证明了使用组合差异集的二阶统计量估计二阶统计的速率估计。本文提出了一种新的方法，以使用相同的亚元素副总会采样器的速度是Nyquist率的两倍的速率重建二阶统计量。我们分析差异集，并得出重量函数的闭合形式表达式和相关图估计的偏差。偏置窗口的主叶宽度约为使用Protope-Prime采样器获得的宽度的一半。由于拟议的方案采用相同的速率原型共同采样器；在一个联合选项期内获得的样品数量和硬件成本不受影响。还描述了具有多个共临界时期的超级估计。此外，从超级nyquist的角度提出了n翼或多层次的共同结构。在这里，估计比nyquist高的Q次估计，其中Q是子采样器的数量。
The theory of co-prime arrays has been studied in the past. Nyquist rate estimation of second order statistics using the combined difference set was demonstrated with low latency. This paper proposes a novel method to reconstruct the second order statistics at a rate that is twice the Nyquist rate using the same sub-Nyquist co-prime samplers. We analyse the difference set, and derive the closed-form expressions for the weight function and the bias of the correlogram estimate. The main lobe width of the bias window is approximately half of the width obtained using the prototype co-prime sampler. Since the proposed scheme employs the same rate prototype co-prime samplers; the number of samples acquired in one co-prime period and hardware cost are unaffected. Super-Nyquist estimation with multiple co-prime periods is also described. Furthermore, n-tuple or multi-level co-prime structure is presented from a super-Nyquist perspective. Here, estimation at a rate q times higher than Nyquist is possible, where q is the number of sub-samplers.
