共素数抽样和互相关估计

Usham V. Dias, Seshan Srirangarajan
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引用次数: 3

摘要

在共素阵列和采样器领域的研究主要集中在从亚奈奎斯特数据中重建信号的自相关和以奈奎斯特速率的频谱内容。这已经在功率谱估计、波束形成、到达方向估计和系统识别中得到了应用。然而,使用互素采样器进行互相关估计并没有受到太多的关注。我们描述了使用协素数采样器的互相关估计,并考虑了两种情况。在第一种情况下,两个信号都是使用协素数采样器获取的,而在第二种情况下,我们假设其中一个信号是已知信号,因此以奈奎斯特速率可用,而第二个信号是使用协素数采样器获取的。我们在每个差值处确定可用于互相关估计的贡献者的数量,因为这是决定估计精度的关键参数。本文所提出的工作将在时延、距离、速度、加速度和需要互相关估计的交叉频谱估计中得到应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Co-Prime Sampling and Cross-Correlation Estimation
Research in the field of co-prime arrays and samplers has been mainly focused on reconstructing the autocorrelation and the spectral content of a signal at the Nyquist rate from sub-Nyquist data. This has found applications in power spectrum estimation, beamforming, direction-of-arrival estimation, and system identification. However, the use of coprime samplers for cross-correlation estimation has not received much attention. We describe cross-correlation estimation using co-prime samplers and consider two scenarios. In the first, both signals are acquired using co-prime samplers, while in the second scenario we assume one of the signals to be a known signal and thus available at the Nyquist rate, and the second signal is acquired using a co-prime sampler. We determine the number of contributors available for cross-correlation estimation at each difference value as this is a key parameter in determining the estimation accuracy. The work presented in this paper will have applications in time-delay, range, velocity, acceleration, and cross-spectrum estimation, which require cross-correlation estimation.
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