The measurement of sea surface profile with X-band coherent marine radar

The line-of-sight velocity of scattering facets is related to the Doppler signals of X-band coherent marine radar from the oceanic surface. First, the sign Doppler Estimator is applied to estimate the Doppler shift of each radar resolution cell. And then, in terms of the Doppler shift, a retrieval algorithm extracting the vertical displacement of the sea surface has been proposed. The effects induced by radar look-direction and radar spatial resolution are both taken into account in this retrieval algorithm. The comparison between the sea surface spectrum of buoy data and the retrieved spectrum reveals that the function of the radar spatial resolution is equivalent to a low pass filter, impacting especially the spectrum of short gravity waves. The experimental data collected by McMaster IPIX radar are also used to validate the performance of the retrieval algorithm. The derived significant wave height and wave period are compared with the in situ measurements, and the agreement indicates the practicality of the retrieval technology.

Optical flow-based method to estimate internal wave parameters from X-band marine radar images

The velocity and direction of internal waves(IWs) are important parameters of the ocean,however,traditional observation methods can only obtain the average parameters of IWs for a single location or large area.Herein,a new method based on optical flow is proposed to derive the phase velocity vectors of IWs from X-band marine radar images.First,the X-band marine radar image sequence is averaged,and ramp correction is used to reduce the attenuation of gray values with increasing radial range.Second,the average propagation direction of the IWs is determined using the two-dimensional Fourier transform of the radar images;two radial profiles along this direction are selected from two adjacent radar images;and then,the average phase velocity of the IWs is estimated from these radial profiles.Third,the averaged radar images are processed via histogram equalization and binarization to reduce the influence of noise on the radar images.Fourth,a weighting factor is determined using the average phase velocity of a reference point;the phase velocities on the wave crest of the IWs are subsequently estimated via the optical flow method.Finally,the proposed method is validated using X-band marine radar image sequences observed on an oil platform in the South China Sea,and the error of the phase velocity is calculated to be 0.000 3–0.073 8 m/s.The application conditions of the proposed method are also discussed using two different types of IW packets.

A new modulation transfer function for ocean wave spectra retrieval from X-band marine radar imagery

When imaging ocean surface waves by X-band marine radar, the radar backscatter from the sea surface is modulated by the long surface gravity waves. The modulation transfer function （MTF） comprises tilt, hydrodynamic, and shadowing modulations. A conventional linear MTF was derived using HH-polarized radar observations under conditions of deep water. In this study, we propose a new quadratic polynomial MTF based on W-polarized radar measurements taken from heterogeneous nearshore wave fields. This new MTF is obtained using a radar-observed image spectrum and in situ buoy-measured wave frequency spectrum. We validate the MTF by comparing peak and mean wave periods retrieved from X-band marine radar image sequences with those measured by the buoy. It is shown that the retrieval accuracies of peak and mean wave periods of the new MTF are better than the conventional MTF. The results also show that the bias and root mean square errors of the peak and mean wave periods of the new MTF are 0.05 and 0.88 s, and 0.32 and 0.53 s, respectively, while those of the conventional MTF are 0.61 and 0.98 s, and 1.39 and 1.48 s, respectively. Moreover, it is also shown that the retrieval results are insensitive to the coefficients in the proposed MTF.

Radar-Based Collision Avoidance for Unmanned Surface Vehicles'

Unmanned surface vehicles （USVs） have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into real- time marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle （V-obstacle） which adjusts the USV＇s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.

Reduction of rain effect on wave height estimation from marine X-band radar images using unsupervised generative adversarial networks

An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion results.The method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework,which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for training.The model is trained by feeding rain-contaminated and clean radar images in an unpaired manner,and the atmospheric scattering model parameters are not required as a prior condition.Additionally,a self-attention mechanism is introduced into the model,allowing it to focus on rain clutter when processing radar images.This combines global and local rain clutter context information to output more accurate and clear de-raining radar images.The proposed method is validated by applying it to actualfield test data,which shows that compared with the wave height derived from the original rain-contaminated data,the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14%using the de-raining method.These results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images.