Noise-Robust Frequency Estimation via Overlapped Sampling-Intervals Zero-Crossing Fitting
The trade-off between noise averaging and temporal resolution fundamentally limits conventional zero-crossing frequency estimators under dynamic and noisy conditions. This paper presents an overlapped sampling-intervals zero-crossing fitting method (OS-ZFM), which introduces a structured overlapping regression framework that decouples noise averaging from temporal update rate. The method adopts a deterministic closed-form formulation, enabling a unified bias-variance analysis to characterize the statistical behavior of the estimator and clarify the role of structured data reuse. Numerical results under intensity and background noise at a signal-to-noise ratio (SNR) of 10 dB show that OS-ZFM reduces median estimation error by more than 60\% compared to conventional zero-crossing fitting methods at the same temporal resolution. It further achieves up to 90\% reduction relative to basic zero-crossing detection and consistently yields lower estimation errors than Hilbert-transform-based estimators. Experimental validation utilizing impact-induced transient motion measured by laser Doppler interferometry demonstrates that OS-ZFM reconstructs smooth and physically consistent trajectories with improved temporal fidelity. Owing to its low computational complexity and deterministic formulation, the proposed method enables accurate real-time frequency and acceleration tracking in resource-constrained measurement systems.