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Harmonic analysis method based on multi-stage frequency correction
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AI
DOI：10.1063/5.0322812.png)
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Abstract
En 中文
Noncoherent sampling in digital storage oscilloscopes introduces spectral leakage and picket-fence effects, which bias harmonic parameter estimates and can obscure weak components in the vicinity of strong tones. This work presents a multi-stage frequency-correction framework for harmonic and interharmonic analysis, consisting of candidate-interval screening in the spectral domain, continuous frequency refinement with a fitness-guided search, least-squares amplitude/phase estimation, and an iterative reconstruct-subtract procedure to progressively suppress leakage-induced interference. A central processing unit-graphics processing unit heterogeneous implementation is further developed to exploit fine-grained data parallelism in interval extraction and batched fitness evaluations. Experimental results demonstrate that the proposed method achieves markedly higher frequency accuracy than a windowed fast Fourier transform baseline under noncoherent conditions (mHz-level maximum absolute error, exceeding 37 & times; improvement over the baseline). Across multi-harmonic cases, it attains accuracy comparable to multiple signal classification (MUSIC) while avoiding the need for sensitive subspace-order selection and other strong prior choices, thereby improving deployment robustness. In challenging adjacent-tone and large-dynamic-range scenarios, where interpolated discrete Fourier transform and MUSIC may exhibit pronounced sensitivity to residual leakage, the proposed iterative cancellation substantially stabilizes weak-component estimation (e.g., amplitude error reduced from tens of percent to the 0.2% level). The framework also supports a continuous accuracy-cost trade-off via the search-budget parameters, providing a practical path toward reliable oscilloscope-resident harmonic/interharmonic measurements.
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ALGORITHM
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