Enhanced Zero-Crossing Detection Method

Traditional zero-crossing detection is a widely-used signal analysis technique that estimates signal frequency or period by counting how many times the waveform crosses the zero axis. However, this method is susceptible to noise interference and exhibits reduced accuracy when processing low-frequency signals or asymmetric waveforms.

The enhanced zero-crossing detection method incorporates multiple optimization strategies to improve detection precision. First, preprocessing techniques such as moving average filters or digital filtering can be implemented to reduce high-frequency noise impacts - for example, applying a simple moving average filter in code: `filtered_signal = conv(signal, ones(1,window_size)/window_size, 'same')`. Second, interpolation algorithms (like linear or quadratic interpolation) are integrated to refine zero-crossing point determination, making detection results closer to actual zero-crossing events. A common implementation involves checking adjacent samples with opposite signs and applying interpolation between them. Additionally, the enhanced method incorporates dynamic threshold adjustment mechanisms to prevent false detections caused by signal amplitude fluctuations, which can be programmed as adaptive thresholds based on signal RMS values.

In real-time signal processing systems, this enhanced approach enables more accurate detection of low-amplitude signals and complex waveforms, making it suitable for various applications including speech recognition, vibration analysis, and power system monitoring. Compared to conventional methods, this optimized solution demonstrates significant improvements in noise immunity and adaptability across different signal conditions.