Xiongbin Wu

Bio: Xiongbin Wu is an academic researcher from Wuhan University. The author has contributed to research in topics: Radar & Bistatic radar. The author has an hindex of 6, co-authored 19 publications receiving 124 citations.

Measurement of Sea Surface Wind Direction Using Bistatic High-Frequency Radar

Abstract: A method for extracting sea surface wind direction information from bistatic high-frequency (HF) radar Doppler spectra is presented. By analogy to the monostatic case, the ratio of the intensities of the positive and negative bistatic Bragg peaks is used to derive the (ambiguous) wind direction. For bistatic operation, the reference is taken with respect to the scattering ellipse normal rather than the radar beam direction. The method is shown to be valid based on simulated bistatic HF radar Doppler spectra. Wind direction is also extracted from the bistatic radar data collected on the Southern China coast. Comparison between the radar-measured wind directions and those obtained from the Advanced Scatterometer shows good agreement.

Direction-of-Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation

Abstract: We consider estimating the direction-of-arrival (DOA) in the presence of sensor array error. In the proposed method, a blind signal separation method, the joint approximation and diagonalization of eigenmatrices algorithm, is implemented to separate the signal vector and the mixing matrix consisting of the array manifold matrix and the sensor array error matrix. Based on a new mixing matrix and the reconstruction of the array output vector of each individual signal, we propose a novel DOA estimation and sensor array error calibration procedure. This method is independent of array phase errors and performs well against difference of SNR of signals. Numerical simulations verify the effectiveness of the proposed method.

Array Beamforming Algorithm for Estimating Waves and Currents From Marine X-Band Radar Image Sequences

Abstract: A new algorithm using array beamforming for estimating waves and currents from marine X-band radar image sequences is proposed. Array beamforming is used to extract the wave directional spectrum from the image sequences; the image intensity time series is considered as the signal received by the “antennas” in conventional array beamforming. To retrieve the current velocity, the following feature is utilized: the total power reaches a maximum when a suitable current is used in the beamforming calculation. Because the new algorithm is based on individual pixel points, as opposed to the full image in the conventional method, the coordinate transformation procedure can be avoided. Because of the filter capacity in beamforming, moving vessels and shadowing have little effect on the estimate results, and the empirical modulation transfer function is not required to amend the wave spectrum. The wave and current retrieval scheme is validated using a simulation and by comparing results with data from an in situ buoy.

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.

Observing Sea States

Abstract: Sea state information is needed for many applications, ranging from safety at sea and on the coast, for which real time data are essential, to planning and design needs for infrastructure that require long time series. The definition of the wave climate and its possible evolution requires high resolution data, and knowledge on possible drift in the observing system. Sea state is also an important climate variable that enters in air-sea fluxes parameterizations. Finally, sea state patterns can reveal the intensity of storms and associated climate patterns at large scales, and the intensity of currents at small scales. A synthesis of user requirements leads to requests for spatial resolution at kilometer scales, and estimations of trends of a few centimeters per decade. Such requirements cannot be met by observations alone in the foreseeable future, and numerical wave models can be combined with in situ and remote sensing data to achieve the required resolution. As today's models are far from perfect, observations are critical in providing forcing data, namely winds, currents and ice, and validation data, in particular for frequency and direction information, and extreme wave heights. In situ and satellite observations are particularly critical for the correction and calibration of significant wave heights to ensure the stability of model time series. A number of developments are underway for extending the capabilities of satellites and in situ observing systems. These include the generalization of directional measurements, an easier exchange of moored buoy data, the measurement of waves on drifting buoys, the evolution of satellite altimeter technology, and the measurement of directional wave spectra from satellite radar instruments. For each of these observing systems, the stability of the data is a very important issue. The combination of the different data sources, including numerical models, can help better fulfill the needs of users.

Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Abstract: Strengths and weakness of remotely sensed winds are discussed, along with the current capabilities for remotely sensing winds and stress. Future missions are briefly mentioned. The observational needs for a wide range of wind and stress applications are provided. These needs strongly support a short list of desired capabilities of future missions and constellations.

Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

Abstract: Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and wave information. The maturity and accuracy of X-band marine radar wind and wave measurements have already enabled relevant commercial products to be used in real-world applications. The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data. Wind measurements are mainly based on the dependence of radar image intensities on wind direction and speed. Wave parameters can be obtained from radar-derived wave spectra or radar image textures for non-coherent radar and from surface radial velocity for coherent radar. In this review, the principles of the methodologies are described, the performances are compared, and the pros and cons are discussed. Specifically, recent developments for wind and wave measurements are highlighted. These include the mitigation of rain effects on wind measurements and wave height estimation without external calibrations. Finally, remaining challenges and future trends are discussed.

Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Abstract: Citation: Bourassa MA, Meissner T, Cerovecki I, Chang PS, Dong X, De Chiara G, Donlon C, Dukhovskoy DS, Elya J, Fore A, Fewings MR, Foster RC, Gille ST, Haus BK, Hristova-Veleva S, Holbach HM, Jelenak Z, Knaff JA, Kranz SA, Manaster A, Mazloff M, Mears C, Mouche A, Portabella M, Reul N, Ricciardulli L, Rodriguez E, Sampson C, Solis D, Stoffelen A, Stukel MR, Stiles B, Weissman D and Wentz F (2019) Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling. Front. Mar. Sci. 6:443. doi: 10.3389/fmars.2019.00443 Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

Abstract: Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.