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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
H. Hu, H. Liu, L. Yang, Z. Yang, R. Jin, and K. Gryllias, “Undersampled signal recovery with uncertainty reduction via non-uniform sampling strategy,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126062
Citation: H. Hu, H. Liu, L. Yang, Z. Yang, R. Jin, and K. Gryllias, “Undersampled signal recovery with uncertainty reduction via non-uniform sampling strategy,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126062
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Undersampled Signal Recovery With Uncertainty Reduction via Non-Uniform Sampling Strategy

doi: 10.1109/JAS.2026.126062
Funds:  This study was supported by the National Natural Science Foundation of China (92360306, 52475129) and the China Scholarship Council
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    Abstract
  • Blade tip timing (BTT) is a promising non-destructive testing technology for online monitoring of rotating blades. Due to physical constraints and measurement principles, BTT signals are highly under-sampled and accompanied by uncertainty and non-uniformity, which complicates subsequent signal processing. This paper reveals the inherent relationship between under-sampling, uncertainty, and non-uniformity. It innovatively transforms the aliasing frequency recovery problem into an uncertainty reduction problem. Undersampling introduces aliasing in the frequency domain, which means the uncertainty of signal recovery increases. De-aliasing is the process of uncertainty reduction, and the non-uniformity is utilized to achieve this goal. Based on this transformed problem, we propose a non-uniform sampling-based uncertainty reduction method (NUS-URM) to perform de-aliasing. Under the framework of Bayesian inference, a non-uniform sampling strategy using two BTT sensors is applied to recover the blade’s natural frequency from the aliased spectrum due to undersampling. Numerical and experimental validations demonstrate the feasibility and accuracy of the NUS-URM for signal recovery under weak prior conditions. The layout of the sensors and prior information are also discussed. After further analyzing the revealed relationships, the NUS-URM has the potential to achieve BTT signal recovery using a single sensor. Additionally, it may also be applied in fields such as computational photography, data compression, astronomical observation, and biomedical signal processing.

     

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