Research On Low Depth Bottom Coherent Doppler Velocimetry Method

There is a strong demand for bottom velocity measurement at low depths in various scenarios such as sonar navigation,velocity measurement,and flow measurement.However,there is currently a problem of low accuracy in bottom velocity measurement at low depths.This article aims to improve the accuracy of bottom velocity measurement at low depths,and conducts research on the model,waveform design method,and velocity information processing method of coherent bottom velocity measurement.This article derives an analytical expression for the standard deviation of velocity measurement of rectangular envelope coherent pulse train signals.This article takes the standard deviation of velocity measurement of the transmitted signal as the optimization objective,and takes the sub pulse width,sub pulse interval,and number of sub pulses of the transmitted signal as the optimization variables to construct an optimization function for the waveform design of coherent pulse train signals.Using the SQP algorithm as the optimization algorithm,the transmission signal parameters were optimized at depths of 4.2m,3.2m,2.2m,and 1.82 m,and water tank experiments were conducted.The experimental results showed that the standard deviation of the optimized parameters for velocity measurement was smaller than that of the comparative parameters,and the accuracy of velocity measurement was higher.This indicates that the optimization function and optimization algorithm are basically effective.This article applies the maximum posterior estimation algorithm to the field of low depth coherent velocity measurement,and further improves the velocity performance.To achieve the maximum posterior estimation algorithm,a measurement model based on Gaussian power spectrum model and a prior model based on AR model were constructed.Smooth the experimental data of the water tank,and the processing results show that the standard deviation and mean square error of the smoothed velocity measurement are smaller than those before smoothing,indicating the effectiveness of the algorithm.