Abstract

Velocity measurement has a high application value in hydrological monitoring and flood disaster warning. The long-distance laser Doppler water flow velocimetry technology has the advantage of strong anti-interference ability and high spatial resolution, and it can realize the high-precision measurement of water flow velocity. Because water flow has low reflectance characteristics, how to extract Doppler frequency from weak non-stationary coherent signals is a crucial problem to be solved to realize long-distance water flow velocity measurement. However, the classical method requires the time domain signal to have high stationarity and is not suitable for processing the coherent signal in the water flow velocity measurement. Aiming at this problem, we proposed a water flow velocimetry method based on adaptive Gaussian weighted integral (AGWI). First, the spectral characteristics of the coherent signal are analyzed in detail, and a statistical model of weak non-stationary signals is established. A second-order Kaiser self-multiplication window (KSMW) is designed to suppress spectral leakage for the asynchronously sampled data. Then, an adaptive homogenization power spectral subtraction (AHPSS) is designed to reduce system noise. Finally, the Doppler spectrum reconstruction and Doppler frequency estimation are performed using the AGWI method to obtain the Doppler frequency, which is further processed to get the water flow velocity. The experimental results show that the method proposed in this paper can achieve accurate and stable measurement of river surface velocity under long-distance conditions.

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