Abstract
This paper presents an improved Doppler-based flow measurement system that applies cross-correlation analysis to enhance the accuracy of open-channel velocity estimation. The proposed approach minimizes turbulence-induced errors and signal phase instability common in conventional Doppler flow meters. The hardware implementation is based on a Raspberry Pi 4 platform integrating 40 kHz ultrasonic sensors and realtime signal processing modules. Field experiments conducted on the Talas River demonstrated that the traditional Doppler method can deviate from reference hydrometric measurements by up to 10%, whereas the cross-correlation technique reduces the deviation to less than 2%. The system's mathematical model and experimental validation confirm that the correlation-based approach ensures stable, precise, and low-cost flow monitoring suitable for long-term hydrological observation and automated water management systems.