High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics Hence the automatic detection of the clutter and interference is an important step towards extracting them In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN) By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy

https://www.mdpi.com/2072-4292/10/10/1517/pdf

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

https://www.mdpi.com/1424-8220/22/8/2822/pdf?version=1649311634

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

In this paper, a novel rectilinearity-based localization method for mixed near-field (NF) and far-field (FF) sources is proposed under unknown mutual coupling. The multiple parameters including direction of arrival (DOA), range and mutual coupling coefficient (MCC) are decoupled, thus only three one-dimensional (1-D) spectral searches are required to estimate the parameters of mixed rectilinear signals successively. Furthermore, the closed-form deterministic Cramer–Rao bound (CRB) of the concerned problem is also derived. Simulation results are provided to demonstrate the effectiveness of the proposed method for the classification and localization of mixed rectilinear sources.

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Mixed Rectilinear Sources Localization Under Unknown Mutual Coupling

This paper presents a novel strategy to simultaneously estimate the direction of arrival (DOA) of a source signal and the phase error of a partly calibrated array with arbitrary geometry. We add up the snapshot data of two different sensors, and then extract a knowledge associated with the DOA and phase errors of these two elements by using singular value decomposition. In such a manner, we can establish a series of linear equations with respect to the unknown DOA and phase error, by simply conducting the procedure on any two sensor elements. On this basis, it can be shown that the problem of jointly estimating DOA and phase error is equivalent to a least square (LS) problem with a quadratic equality constraint. To solve this LS problem (so that the DOA and phase error can be obtained), an effective convex–concave procedure is employed. Different from the conventional algorithms that are limited to specific array geometries, the proposed one is suitable for arrays with arbitrary geometries. More importantly, the devised method only requires one extra calibrated sensor, which is not necessarily adjacently located with the reference one. Several simulations are carried out in this paper and the effectiveness of the devised method can be clearly observed.

DOA and Phase Error Estimation for a Partly Calibrated Array With Arbitrary Geometry

Physical oceanography is the study of physical conditions, processes and variables within the ocean, including temperature–salinity distributions, mixing of the water column, waves, tides, currents...

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Progress in satellite remote sensing for studying physical processes at the ocean surface and its borders with the atmosphere and sea ice

We explore the potential of computing coastal ocean surface currents from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery using the maximum cross-correlation (MCC) method. To improve on past versions of this method, we evaluate combining MODIS and VIIRS thermal infrared (IR) and ocean color (OC) imagery to map the coastal surface currents and discuss the benefits of this combination of sensors and optical channels. By combining these two sensors, the total number of vectors increases by 58.3 % . In addition, we also make use of the different surface patterns of IR and OC imagery to improve the tracking performance of the MCC method. By merging the MCC velocity fields inferred from IR and OC products, the spatial coverage of each individual MCC field is increased by 65.8 % relative to the vectors derived from OC images. The root mean square (RMS) error of the merged currents is 18 cm · s − 1 compared with coincident HF radar surface currents. A 5-year long time serious of merged MCC computed currents was used to investigate the current structure of the California Current (CC). Weekly, seasonal, and 5-year mean flows provide a unique space-time picture of the oceanographic variability of the CC.

https://www.mdpi.com/2072-4292/9/10/1083/pdf

Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery

This paper proposes a method for extracting the lateral velocity in multitarget cases to improve the ship tracking capability of high-frequency surface wave radar (HFSWR). The method exploits the phase interferometry between two antenna arrays that is done in the time domain after joint frequency–azimuth filtering. First, a frequency filter based on generalized S transform with a variable factor is applied to eliminate the background noise. The variable factor is linked to the target's radial velocity and can control the time and frequency resolutions. We then use two identical subarrays as two receiving channels to implement the azimuth filtering. Based on adaptive minimum-variance distortionless response beamforming, the extraneous signals can be suppressed. Subsequently, the varying slope of the phase difference of the target of interest signal from two channels can be extracted to compute the lateral velocity in a low signal-to-noise ratio scenario. The interference of noise and extraneous target signals is analyzed using simulations, and the effectiveness of the frequency–azimuth filtering method is validated with both simulations and experimental data. In addition, the high spatial resolution ship tracking results over an hour match well with automatic identification system data, demonstrating the feasibility of tracking targets with HF monostatic radar.

Jianfei Liu

Papers

Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery

A Scheme for Multitarget Lateral Velocity Measurement With High-Frequency Monostatic Radar