Application of EEMD combined with cross-correlation algorithm in Doppler flow signal

To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters, a novel signal processing algorithm that combines ensemble empirical mode decomposition (EEMD) and cross-correlation algorithm was proposed . Firstly, a fast Fourier transform (FFT) spectrum analysis was utilized to ascertain the frequency range of the signal. Secondly, data acquisition was conducted at an appropriate sampling frequency, and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions (IMFs) by EEMD. Subsequently, these decomposed IMFs were recombined based on their energy entropy, and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering. Finally, an ideal ultrasonic Doppler flow rate signal was extracted. Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio (SNR) and extend the lower limit of measurement of the ultrasonic Doppler flow meter.