Extraction And Prediction Of Ocean Surface Dynamics Elements Using A HFSWR Under Multiple Sea States In Remote Sea Areas

By employing the propertie of low attenuation of high frequency electromagnetic wave(HFEW)propagating on the sea surface,the high frequency surface wave radar(HFSWR)can achieve large-area,all-weather,and real-time monitoring of coastal area.Therefore HFSWR is highly valued by countries around the world.HFSWR can be divided into wide-beam HFSWR and narrow-beam HFSWR,where the narrow-beam HFSWR can not only provide the position and motion information of the over-the-horizon target,but also perform fine extraction of ocean surface dynamics elements(OSDEs)in remote sea areas which are ocean current velocity,significant wave height,wind speed,and wind direction etc.Narrow-beam HFSWR is able to monitor larger area and has better azimuth resolution,hence it has broad application prospect.At present,HFSWR ocean current velocity extraction technology is very mature and has been commercialized.However the significant wave height and wind speed extraction technologies need to be further studied.This paper takes single-station narrow-beam HFSWR as the study platform,takes the significant wave height and wind speed as the research objects,and mainly studies the algorithms for extracting significant wave height and wind speed under multiple sea states in remote sea areas.Furthermore,we carry out the research on the joint prediction of three-dimensional ocean surface dynamics element(3D-OSDE)which consists of ocean current velocity,significant wave height,and wind speed for the first time.The research results provide theoretical basis and methods for achieving target and marine compatible detection,and are helpful to develop a set of signal processing methods that can simultaneously extract and predict OSDEs based on our school's existing target detection HFSWR system.The main research contents are as following:1.Research on significant wave height extraction method based on the first-order spectrum.Firstly,the significant wave height extraction methods used in single-site narrow-beam HFSWR are introduced in detail,including Barrick algorithm and the first-order Bragg peaks-based method,and the problems in these methods are pointed out.Then a new method is proposed which is based on the first-order spectrum.The method can break through the lower limit of Barrick algorithm and solve the problem of the first-order Bragg peaks-based method.2.Research on significant wave height extraction method under multiple sea states in remote sea areas.Generally,a low-frequency HFSWR is more suitable to extract the OSDEs at a distance.However,Barrick algorithm,the new method based on the first-order spectrum,and the significant wave height extraction method based on dual-frequency fusion mode are not able to extract significant wave height using a low-frequency HFSWR under multiple sea states in remote sea areas.Therefore we employ artificial intelligence technology to fully integrate Barrick algorithm and the new method based on the first-order spectrum,and propose the significant wave height extraction method based on single-frequency fusion mode which can extract significant wave height using a low-frequency HFSWR under multiple sea states in remote sea areas.Simulations on real-world data show the significant wave height can be successfully extracted by the method and coincide well with the buoy significant wave height.Then the classification method in the significant wave height extraction method based on single-frequency fusion mode is further studied.Since feedforward neural network(FNN)has limited modeling capability and low classification accuracy for time series data,a classification algorithm based on long-short-term memory neural network(LSTMNN)is proposed.Because the gradient descent(GD)algorithm often suffers from a slow convergence speed and poor local optima,an unscented Kalman filter(UKF)based training algorithm is developed.Simulations on real-world data show the algorithm can achieve better performance.3.Research on wind speed extraction method under multiple sea states in remote sea areas.After obtaining the significant wave height,the wind speed can be extracted by employing the parametric or non-parametric models.However the parametric models have poor fitting and generalization performance.Moreover the commonly used non-parametric models can not make full use of the significant wave height information at historical moments,are not suitable for time series with timing-dependent characteristics of different lengths,and the input vector length is difficult to determine.Therefore we propose a wind speed extraction method based on the LSTMNN and the method can extract wind speed under multiple sea states in remote sea areas.The method can absorb timing-dependent characteristics into its feedback connections,make full use of the significant wave height information at historical moments,and does not need to determine the input vector length.Therefore this method is an effective way to solve the above problems.4.Research on prediction methods of OSDEs extracted by HFSWR.There is no related literature on the prediction of 3D-OSDE and this research is a new exploration in this field.Based on the latest research results in the field of multi-dimensional signal processing,we perform joint prediction of 3D-OSDE in quaternion domain and propose a 3D-OSDE joint prediction method based on quaternion-valued neural network(QNN).Considering that the 3D-OSDE time series is highly nonstationary under multiple sea states,we propose an online training method based on second-order derivative to adjust the parameters of QNN in real time.