Information Extraction Of Sea Surface Wind And Target With Compact HF Surface Wave Radar

The real-time monitoring of the ocean provides a necessary guarantee for human beings to understand and control the ocean.As an important remote sensing tool for ocean surface compact high frequency surface wave radars(HFSWR)are widely used in the world,due to its advantages of large monitoring area,all-weather,easy erection and easy maintenance.The marine monitoring of the compact HFSWR focuses on the parameters extraction of the soft targets such as wind,wave and current,and the monitoring of the ships on the sea.Wind speed is one of the important ocean state parameters.Accurate extraction of sea surface wind speed is an important guarantee for achieving marine environmental monitoring and coastal engineering applications.However,at present,there are still challenges in wind speed extraction with HFSWR.Traditional wind speed inversion methods based on empirical models are often limited by sea area and sea conditions.In this paper,considering the persistence and accumulation of the wind's effect on the sea surface and the strong nonlinear processing ability of the artificial network,a new wind speed extraction method is proposed.The proposed method based on artificial neural network which can be trained by historical sea state data measured by buoys to achieve non-linear mapping among wind and effective wave height,wave period,wind direction,time.The test results show the stability of the trained network both in time and space and the trained network was applied to the wind speed inversion of the high Frequency surface wave radar,OSMAR-S.The correlation coefficient between the inversion wind speed and the measured wind speed of the buoy reaches 0.849,and the root mean square error is 2.11 m/s.This result is significantly better than the conventional method which inverts the wind speed from wave height,and verifies the feasibility of this method in HF radar wind speed inversion.Ship detection at the sea surface is also important for improving human marine activities and safety control.Most existing ship detection methods for HFSWR are based on peak and constant false alarm rate detection.Such methods require the target signal to be stationary in the coherent integration time of several minutes.However,it is difficult for the ships to satisfy the stationary assumption in such a long period.In addition,the traditional direction finding method uses samples at a fixed Doppler frequency,and then estimates the direction of arrival of the ships using a super-resolution algorithm.For a non-stationary ship echo,such a sampling process may cause the sampled values to deviate from the instantaneous frequency and miss part of the energy,resulting in a large direction finding error.To account for the non-stationary property,a time-frequency(TF)analysis based ship detection and direction finding method is proposed.Firstly,TF spectrum of the echo signal is achieved by applying the synchronous extraction transform,and target ridges on the TF plane are extracted using the boundary tracking algorithm.Then,the samples of the linear TF representation along the target ridges are collected to form the array snapshots for direction estimation.The processing results of the radar data show that the proposed method outperforms the constant false alarm rate method within both increased detection rates and decreased direction finding errors,especially under relatively low signal-to-noise ratio scenarios.