Application Research Of Kalman Filter In Doppler Velocity Measurement

Acoustic Doppler velocity sonar is an important ocean observation device that accurately measures the bottom/water three-dimensional velocity using the Doppler Effect.It has been widely used in marine resources development,ship navigation,flow measurement and many other fields.The continuous improvement of the demand for work,the problem of accurate speed measurement has been concerned by people.In this paper,we focus on the speed estimation problem in unknown non-stationary velocity measurement noise,and study the method of improving the accuracy of velocity measurement from the perspective of adaptive filtering.The sonar performance is severely affected by the environment,and the unknown non-stationary noise assumption is more in line with the acoustic model of the acoustic Doppler velocity sonar.The method of velocity estimation or robustness velocity estimation based on the "thick tail" noise distribution solves the shortcomings of the classical Kalman filter.However,since the parameters need artificial design,the practical application has certain limitations.In this paper,based on the variational Bayesian method,the noise statistical model is studied to realize the real-time inference of unknown non-stationary velocity measurement noise.Based on this,the variational Bayes Adaptive Kalman Filter(VBAK)is used.Achieve an optimal estimate of velocity information.We evaluate the performance of VBAK algorithm from the following two aspects: 1)Construct two typical velocity measurement scenarios,and quantitatively evaluate VBAK algorithm through directness index(RMSE ? CEE% ? EE%)and indirect index(estimation error and true error correlation coefficient);2)The actual application effect of the VBAK algorithm is evaluated by processing the field test data.The results show that the VBAK algorithm has obvious advantages over the classical Kalman filtering algorithm,and can solve the problem of acoustic Doppler velocimetry information estimation under unknown non-stationary noise conditions.The estimation accuracy is further improved and the expected target is achieved.