Miao Li

Bio: Miao Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Radar & Azimuth. The author has an hindex of 5, co-authored 9 publications receiving 44 citations.

Extraction of Wind Direction Spreading Factor From Broad-Beam High-Frequency Surface Wave Radar Data

Abstract: The spreading factor is considered as a key parameter that controls the concentration of the directional distribution of the wave energy. It has been confirmed by many scholars that there is a certain relationship between spreading factor and sea surface wind. In the application of high frequency surface wave radar (HFSWR), spreading factor is extracted from the ratio ( $R_{B}$ ) of power spectrum density (PSD) of positive ( $P^{+}_{B}$ ) and negative ( $P^{-}_{B}$ ) Bragg peaks. To extract accurate spreading factor, the premise is that the PSD of detection unit is as little as possible affected by the adjacent detection units. For narrow-beam radar, digital beamforming (DBF) is easy to meet requirements. But for broad-beam radar, it is very difficult. In this paper, a new scheme is proposed to extract spreading factor from broad-beam HFSWR data with the MUSIC-APES algorithm. Different from spatial filtering by DBF, MUSIC-APES directly estimates the azimuth of positive or negative Bragg waves and their echo amplitudes. For broad-beam radar, this scheme can still achieve high azimuth resolution and accurate amplitude estimation at the same time. It solves the biggest obstacle to extract the spreading factor from broad-beam HFSWR data. To verify the feasibility of this scheme, simulations and experiments are carried out to compare with DBF. The extraction accuracy is improved greatly. The results are very surprising. It shows that spreading factor and wind speed are highly relevant. This may be a new way to extract wind speed in the application of HFSWR.

Ocean Surface Current Extraction Scheme With High-Frequency Distributed Hybrid Sky-Surface Wave Radar System

Abstract: The high-frequency hybrid sky-surface wave radar (HF HSSWR) has recently been used to monitor large-area sea states. However, most of the HF HSSWR detection methods are based on the assumption of a no-tilt and constant height ionospheric model, and the influences caused by uneven electron density are ignored. This paper proposes a new surface current inversion scheme for the HF distributed HSSWR system, which considers the unknown ionospheric state as a black box and extracts the key parameters to compute the surface current based on a scattering model. The computational formula of the component of the current vector is explored using spatial scattering theory instead of an approximate bistatic model. In addition, the Fourier series expansion method is applied to the HF data to extract the real first-order Bragg frequency. Subsequently, the grazing angle and the bistatic angle can be found by inversion using the first-order Bragg frequency formula after searching out the common scattering patch of two receiving stations. Simultaneously, the coordinate registration of the currents can also be determined. The feasibility and effectiveness of this new algorithm are verified with field experimental results by comparing the current vectors derived from HSSWR and traditional HF SWR. The RMS differences of the magnitude and direction of the current vectors within the core common area of the two detection systems are about 10.2 cm/s and 9.5°, respectively.

Computing Ocean Surface Currents From GOCI Ocean Color Satellite Imagery

Abstract: One of the significant challenges in physical oceanography is getting an adequate space/time description of the ocean surface currents. One possible solution is the maximum cross-correlation (MCC) method that we apply to hourly ocean color images from the Geostationary Ocean Color Imager (GOCI) over five years. Since GOCI provided a large number of image pairs, we introduce a new MCC search strategy to improve the computational efficiency of the MCC method saving 95% of the processing time. We also use an MCC current merging method to increase the total spatial coverage of the currents, proving a 25% increase. Five-year mean and seasonal time-average flows are computed to capture the major currents in the area of interest. The mean flows investigate the Kuroshio path, support the triple-branch pattern of the Tsushima Warm Current (TC), and reveal the origin of the TC. The evolution of a warm core ring shed by the Kuroshio near the northeast coast of Honshu, Japan, is clearly depicted by a sequence of three monthly MCC composites. We capture the evolution of the Kuroshio meander over seasonal, monthly, and weekly time scales. Three successive weekly MCC composite maps demonstrate how a large anticyclonic eddy, to the south of the Kuroshio meander, influences its formation and evolution in time and space. The unique ability to view short space/time scale changes in these strong current systems is a major benefit of the application of the MCC method to the high spatial resolution and rapid refresh GOCI data.

A New Algorithm for Surface Currents Inversion With High-Frequency Over-the-Horizon Radar

Abstract: The conventional method of extracting ocean surface currents by high-frequency over-the-horizon radar is based on the fixed first-order Bragg frequency formula and ignores the effects caused by the environment, especially in near-shore areas. In this letter, a current inversion model based on 2-D Fourier series expansion was developed. The first-order Bragg frequency and the Doppler offset induced by radial current are dealt with the bivariate functions of group distance and azimuth angle in the proposed method. By solving an overdetermined matrix equation with the least-square fitting method, the current at each detection grid can be estimated. As the Bragg frequency obtained by this new algorithm is adaptive to the environment, the accuracy of current measurement will be improved. The feasibility and effectiveness of the new method are verified with simulations and experimental results. The currents estimated by the traditional method and the new algorithm are compared with two in situ buoys. Results indicate that the new algorithm possesses comparable accuracy for far-shore areas and better accuracy for near-shore areas when compared with the conventional method.

Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery

Abstract: 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.

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

Abstract: 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

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

Abstract: 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.

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

Abstract: 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...

MFFN: An Underwater Sensing Scene Image Enhancement Method Based on Multiscale Feature Fusion Network

Abstract: Vision-guided autonomous underwater vehicles based on remote sensing play an important role in ocean missions. However, some problems exist in underwater visual perception, such as color distortion, low contrast, and fuzzy details, which restrict the applications of underwater visual tasks. Most of the state-of-the-art image enhancement methods are still limited in scene adaptability, recovery accuracy, and real-time processing. To solve these problems, we propose an underwater sensing scene image enhancement method called a multiscale feature fusion network (MFFN). To extract the multiscale feature, the measure merging the feature extraction module, the feature fusion module, and the attention reconstruction module is designed. This measure can also enhance the adaptability and visual effect of the scene. Moreover, we propose multiple objective functions for supervised training to match the nonlinear mapping. Based on the qualitative and quantitative evaluations, the proposed method produces competitive performance compared with some state-of-the-art methods, and the perception and statistical quality of underwater images are enhanced effectively.