单元间距压缩的次奈奎斯特互素感知--低延迟方法
Sub-Nyquist Co-Prime Sensing with Compressed Inter-Element Spacing -- Low Latency Approach
摘要：
压缩单元间距的互质阵列（CACIS）是互质阵列的一种推广。在这种情况下，元件间间距可以变化。原型互质数组和嵌套数组是CACIS方案的特殊情况。在本文中考虑了以前没有解决的问题。CACIS配置的差集的基本原理是针对低延迟开发的。此外，封闭形式的表达式的权重函数（有助于估计的自相关的样本数）和偏差窗口的相关图估计，这是以前未知的，推导出来。理想情况下，偏置窗口应该是脉冲。提供了几个例子，随着模拟来验证所提出的索赔。所有可能的样本对都用于估计，这提供了低延迟。作为一个应用，时间谱被认为是模拟。
英文摘要：
Co-prime arrays with compressed inter-element spacing (CACIS) is one of the generalizations of the co-prime array. The inter-element spacing can be varied in this case. The prototype co-prime arrays and nested arrays are a special case of the CACIS scheme. The problems that were not addressed previously are considered in this paper. The fundamentals of the difference set for the CACIS configuration are developed for low latency. In addition, the closed-form expressions for the weight function (number of samples that contribute to estimate the autocorrelation) and bias window of the correlogram estimate, which were previously unknown, are derived. Ideally, the bias window should be an impulse. Several examples are provided along with simulations to verify the claims made. All possible sample pairs are used for estimation, which provides for low latency. As an application, temporal spectrum is considered for simulations.
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