Low-latency stage-adaptive cascade architecture for real time non-stationary noise filtering

In many real-time measurement and monitoring systems, the quality of acquired signals is often severely degraded by complex environmental noise sources with non-stationary properties, rendering analysis, important feature extraction, and decision-making unreliable. This study proposes a multi-stage adaptive denoising architecture based on the least mean square (LMS) algorithm, in which the number of filter stages and the step size are automatically adjusted according to error statistics, the remaining correlation between the residual and the reference signal, and the real-time signal-to-noise ratio (SNR) of the signal. The stopping mechanism is determined by a two-tailed Fisher-z correlation test, with effective sample size correction in the presence of autocorrelation and modulation based on SNR, to ensure the stability of the adaptive system against non-stationary noise. The filter is evaluated on simulated signal datasets and real-world measured data. Compared with the conventional LMS filter configuration under the tested simulated conditions, the proposed architecture reduces mean squared error (MSE) by 38–82% and mean absolute error (MAE) by 15–45%, while improving both SNR and peak signal-to-noise ratio (PSNR). The execution time of the proposed method is approximately 3.5–4 times lower than that of the fixed-threshold method under the tested settings. These results indicate that the proposed method can improve the trade-off between denoising performance and computational efficiency, showing potential for low-latency implementation on resource-constrained devices.