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Volume 46 Issue 3
Mar.  2024
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HUANG Xiangdong, SONG Jinshui, LI Yanping. High Accuracy Carrier Frequency Estimation of Multi-band Communication Signals Based on Undersampling[J]. Journal of Electronics & Information Technology, 2024, 46(3): 906-913. doi: 10.11999/JEIT230297
Citation: HUANG Xiangdong, SONG Jinshui, LI Yanping. High Accuracy Carrier Frequency Estimation of Multi-band Communication Signals Based on Undersampling[J]. Journal of Electronics & Information Technology, 2024, 46(3): 906-913. doi: 10.11999/JEIT230297

High Accuracy Carrier Frequency Estimation of Multi-band Communication Signals Based on Undersampling

doi: 10.11999/JEIT230297
Funds:  General Program of Foundation Research Plan of Qinghai Province, China (2021-ZJ-910)
  • Received Date: 2023-04-18
  • Rev Recd Date: 2024-01-24
  • Available Online: 2024-02-07
  • Publish Date: 2024-03-27
  • To essentially overcome the 3 deficiencies of the mainstream Modulation Wideband Converter (MWC )-based undersampling frequency estimator (i.e., over-consumption of undersampling channels, low accuracy of carrier frequency estimation, high sparsity of the source distribution), this paper proposes the phase-difference corrector based on coprime spectral analysis for the carrier frequency estimation of multi-band communication signals. Specifically, by substituting the multi-path MWC undersampling with the 2-path coprime undersampling, the consumption of undersampling channels is greatly reduced; Further, by developing the mapping relationship between the panoramic spectrum peak indices and the IDFT index pairs of coprime analyzers, the phase difference of the adjacent snapshots’ IDFT outputs corresponding to these index pairs can be analytically extracted, thus achieving much higher estimation accuracy compared to the mainstream MWC method. Meanwhile, by means of incorporating the minimum-sized half-decomposition based all-phase filter design into the prototype filter design, a two-path paralleled coprime spectral analyzer can be constructed, which thoroughly gets rid of the dependency of the high sparsity of the source distribution. Numerical results show that, compared to the mainstream MWC method, the proposed spectral corrector’s estimation error is no more than 1/20 of the former, while only consuming less than half of the sample amount.
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