Interpolation and Decimation for Multi-rate Signal Processing

Polyphase filters break down signal-processing complex multipliers into simple sub-filters to yield computational system efficiency. In decimation, since computationally intensive filtering will be executed using these filters, it reduces processing time and power consumption by processing only the required samples [6]. Polyphase filters dramatically reduce the number of calculations necessary for down-sampling by factoring filtering operations [7]. For interpolation, polyphase filters are used to make upsampling less costly in terms of filtering [7]. In this work, an innovation is introduced to accurately reconstruct high-rate signals without the overhead of processing every sample at the higher rate, remaining optimal with respect to memory usage and computational performance [8]. Additionally, whenever the sampling rates change, these designs can benefit from adaptive polyphase filter design, leading to greater responsiveness and improved resource efficiency in real-time applications [8].

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-28
DOI
https://doi.org/10.5281/zenodo.14234791
Primary Topic
Digital Filter Design and Implementation
Type
article
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article

Interpolation and Decimation for Multi-rate Signal Processing

Zenodo (CERN European Organization for Nuclear Research)
Digital Filter Design and Implementation
article

Interpolation and Decimation for Multi-rate Signal Processing

article en

Abstract

Polyphase filters break down signal-processing complex multipliers into simple sub-filters to yield computational system efficiency. In decimation, since computationally intensive filtering will be executed using these filters, it reduces processing time and power consumption by processing only the required samples [6]. Polyphase filters dramatically reduce the number of calculations necessary for down-sampling by factoring filtering operations [7]. For interpolation, polyphase filters are used to make upsampling less costly in terms of filtering [7]. In this work, an innovation is introduced to accurately reconstruct high-rate signals without the overhead of processing every sample at the higher rate, remaining optimal with respect to memory usage and computational performance [8]. Additionally, whenever the sampling rates change, these designs can benefit from adaptive polyphase filter design, leading to greater responsiveness and improved resource efficiency in real-time applications [8].

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 64%
Digital Filter Design and Implementation
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