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Efficient Sampling Rate Offset Compensation - an Overlap-Save Based Approach

机译:高效采样率偏移补偿-一种基于重叠保存的方法

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Distributed sensor data acquisition usually encompasses data sampling by the individual devices, where each of them has its own oscillator driving the local sampling process, resulting in slightly different sampling rates at the individual sensor nodes. Nevertheless, for certain downstream signal processing tasks it is important to compensate even for small sampling rate offsets. Aligning the sampling rates of oscillators which differ only by a few parts-per-million, is, however, challenging and quite different from traditional multirate signal processing tasks. In this paper we propose to transfer a precise but computationally demanding time domain approach, inspired by the Nyquist-Shannon sampling theorem, to an efficient frequency domain implementation. To this end a buffer control is employed which compensates for sampling offsets which are multiples of the sampling period, while a digital filter, realized by the well-known Overlap-Save method, handles the fractional part of the sampling phase offset. With experiments on artificially misaligned data we investigate the parametrization, the efficiency, and the induced distortions of the proposed resampling method. It is shown that a favorable compromise between residual distortion and computational complexity is achieved, compared to other sampling rate offset compensation techniques.
机译:分布式传感器数据采集通常包含单个设备的数据采样,其中每个设备都有自己的振荡器来驱动本地采样过程,从而导致各个传感器节点的采样率略有不同。但是,对于某些下游信号处理任务,即使是较小的采样率偏移也必须进行补偿,这一点很重要。然而,对准仅百万分之几的振荡器的采样率是有挑战性的,并且与传统的多速率信号处理任务完全不同。在本文中,我们建议将受Nyquist-Shannon采样定理启发的精确但计算量大的时域方法转换为有效的频域实现。为此,采用缓冲控制来补偿采样偏移,该采样偏移是采样周期的倍数,而由众所周知的Overlap-Save方法实现的数字滤波器则处理采样相位偏移的小数部分。通过对人为失准数据的实验,我们研究了所提出的重采样方法的参数化,效率和引起的失真。结果表明,与其他采样率偏移补偿技术相比,在残留失真和计算复杂度之间实现了良好的折衷。

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