Coherent S-band radar has recently been emerged as a promising technique for ocean surface wave and current detection. It can measure ocean surface current by estimating Doppler frequency shifts from sea surface signals. However, the conventional time averaging (TA) method neglects spatial dimension information and is unavailable under low wind speed condition. Two algorithms for ocean current inversion are proposed in this letter: the spatial-temporal averaging (STA) method and the wavenumber-frequency (WF) method. In the STA method, the TA method is extended to the spatial-temporal domain. This approach fully exploits the spatial continuity of radar signals. In the WF method, a 2-D Fast Fourier Transform (2-D FFT) is applied to transform the spatial-temporal radial velocities into the wavenumber-frequency domain. After employing dual filtering to eliminate nonlinear components, the radial current velocity is estimated through a modified dispersion relation fitting. The two methods are based on different physical mechanisms: the STA method measurements include wind drift components, while the WF method remains unaffected by wind drift. Therefore, wind drift can be effectively estimated by calculating the difference between the two methods’ measurements. Validation using observational data collected at Beishuang Island during Typhoon Catfish shows that the estimated wind drifts achieve a correlation coefficient (COR) of 0.90 with the “empirical model predictions”. This confirms the effectiveness of the proposed algorithms.

In this letter, we propose a novel methodology for retrieving wave parameters, i.e., significant wave height and mean wave period, in near-nadir looking mode using a 77 GHz frequency-modulated continuous-wave (FMCW) multipleinput– multiple-output (MIMO) radar. First, the range-Doppler spectrum is estimated from the raw radar data, and the time-Doppler spectrum in the desired direction is obtained by integrating the digital beamforming algorithm with MIMO array techniques. Next, the radial velocity series are calculated using the spectral moment method. A Fourier transform is then applied to estimate the wave height spectrum from the radial velocity series, and the significant wave height and mean wave period can be obtained by the moment estimation method. Finally, the results obtained from numerical simulations and sea surface observations demonstrate that the retrieval method can extract wave parameters with reasonable performance at small incidence angles (0∼18°).

For shipboard coherent S-band radar, the shadow modulation phenomenon happens at large sea surface undulations or small grazing angles. As the ship speed increases or the sea state rises, the shadow modulation leads to an increase in the peak value of the wave spectrum, which results in inaccurate wave parameter inversion. In order to achieve accurate wave detection in high sea state or high ship speed scenarios, a shadow modulation correction method based on velocity-fitting relationship and Hermite interpolation is presented. First, the variable range of radial velocity is computed using the simulated sea surface’s velocity fitting relation. The anomalous Doppler velocity is then rectified by applying the modified Akima piecewise cubic Hermite interpolation (MAPCHI) algorithm. The MAPCHI algorithm is appropriate for complex nonlinear curves and is not prone to abrupt fluctuations or flattening issues. The implementation of this interpolation algorithm can alleviate the problem of anomalous fluctuations in Doppler velocities induced by shadow modulation. Finally, the nondirectional wave spectrum and wave parameters are calculated. Numerical simulation experiments show that the proposed method can rectify the influence of shadow modulation at various speeds and sea state backdrops. Meanwhile, comparing the radar measurements in the South China Sea with the buoy results, the mean absolute error (MAE) of significant wave height and mean wave period are 0.21 m and 0.34 s, respectively. The results demonstrate that the proposed method can successfully remove shadow modulation interference with high robustness.

Shipboard coherent microwave radar is an emerging tool for ocean observation, which utilizes the direct relationship between the orbital wave velocity and the wave height spectrum to retrieve wave parameters. However, the ship’s motion and broken waves would introduce extra Doppler components into the Doppler spectrum of the sea echo, and accordingly degrade the performance of wave measurements. Consequently, the Doppler components should be estimated before the inversion of ocean wave parameters. To address this problem, a Doppler frequency components estimation method, which describes the problem as an optimization problem with constraints to fit the raw Doppler spectrum, is proposed. First, the Doppler spectrum model for shipboard coherent microwave radar is parameterized and simplified. Then, an objective function and the constraints are established based on the generation mechanism of frequency components in the Doppler spectrum to make the reconstructed Doppler spectrum fit the raw Doppler spectrum. Subsequently, the particle swarm optimization (PSO) algorithm is used to find the optimal coefficient to solve the optimal solution. At last, frequency components, which are produced by ship motion, broken waves, and orbital modulation of gravity waves, are estimated. To validate the proposed method, the simulation data and the experimental data collected with a shipboard S-band radar in the South China Sea in December 2020 are analyzed. The estimated frequency components from radar data are compared with the MTi-G-measured and buoy-measured data. The results indicate that the proposed method is effective for estimating the frequency components and could improve the performance of wave measurements.

In the study of deterministic sea waves, the predictable zone refers to the area where the phase-resolvable wave field can be fully reconstructed and accurately predicted based on measured data. Under certain marine environmental and radar measurement parameter conditions, expanding the predictable zone is of great significance for domains such as quiescent period prediction (QPP) for ships and optimizing wave energy harvesting. Prior research on the predictable zone mostly relied on either temporal or spatial measurements, significantly constraining the range of the predictable zone. In this paper, based on coherent microwave radar, we present a novel method combining spatial and temporal measurements to expand the predictable zone. Firstly, the boundary of the predictable zone under a single spatial measurement and multiple spatial measurements (spatial-temporal measurement) are analyzed, and the closed-form expressions for the predictable zone are derived for both unidirectional and multidirectional wave fields. The quantitative relationship between the predicted time at the desired spatial location and the radar parameters and wave spectra is obtained. Subsequently, the theoretical analysis of the predictable zone is validated using the numerical simulation by comparing the theoretically predictable zone with the error distributions for the unidirectional and multidirectional wavefield cases. Finally, the analysis of experimental data from Weihai in 2021 also indicates that the joint spatial-temporal measurements can effectively expand the predictable zone.

The reconstruction and prediction of deterministic sea waves are important for increasing the safety of offshore operations and improving the efficiency of wave energy conversion. Coherent microwave radar, as a commonly used instrument for sea surface observation, can directly reconstruct deterministic sea waves based on the Doppler echo. However, its reconstruction performance relies heavily on the accuracy of the velocity measurements and is severely limited in a high sea state or the presence of hard target clutter. In this work, we propose a robust deterministic wave reconstruction and prediction scheme to reduce the influence of velocity measurement anomalies on the reconstruction and prediction results. First, the spatial-temporal velocities are obtained from the coherent microwave radar, followed by the least absolute deviation (LAD) method for solving the parameters of the wave motion model, and the deterministic wave is reconstructed and predicted based on the obtained model parameters. The deterministic wave reconstruction and prediction under long-crested and short-crested sea states with broken waves are simulated, and the results confirm the effectiveness of the scheme. Simultaneously, the scheme is verified using the experimental data collected with a coherent S-band radar in Weihai in December 2021. First, the significant wave height is reconstructed using the scheme, and the correlation with thein-situdata reaches 0.97. Then, the prediction performance of deterministic sea waves is compared with the velocity potential function method, and these results confirm the effectiveness of the scheme.

Accurate prediction of the ship motion attitude in the future period is important to ensure the safety of offshore operations and sea navigation. Coherent S-band radar is a novel wave monitoring device that can be applied to advance the development of ship motion attitude prediction. In this study, we propose a method for realizing ship motion attitude prediction from radial velocity of water particle using coherent S-band radar. First, the parameters of the wave motion model (WMM) are estimated using the regularized least squares (RLS) method, which can solve the ill-conditioning matrix on the estimation accuracy under the short-crested wave sea state. Then, a predictable zone model considering radar dwell time is proposed based on coherent S-band radar, which can improve the traditional predictable zone. Finally, the deterministic sea waves in the predictable zone are input into the ship response amplitude operator (RAO) to realize the ship motion attitude prediction. The performance of the proposed method for ship motion attitude prediction in short-crested wave condition is analyzed through simulation. Meanwhile, the proposed method is validated using real data from the sea trial experiment in December 2019 in the South China Sea. The prediction results obtained by the proposed method are compared with the measured data from the MTi-G-710 instrument, and the two trends are consistent with a correlation coefficient of 0.7564. The results show that the proposed method can effectively realize ship motion attitude prediction using coherent S-band radar.

Shipboard coherent microwave radar is a rapidly emerging tool for detecting the physical characteristics of ocean waves. However, the scattering mechanism for coherent microwave radar on ship has never been established for developers to put this type of radar into extensive use. To address the problem, a model incorporated with free and broken waves for shipboard coherent microwave radar is then proposed. Six-degree-of-freedom motion and forward velocity of the ship, which can reflect the real-life motion of the ship, are derived into three-coordinate ship velocity components. Then the three-coordinate ship velocity components combined with the azimuthal angle between the radar look direction and the forward direction of the ship (ABRF) are converted into the radial velocity integrated into the shipboard coherent microwave radar model. The characteristics in the Doppler spectrum of the shipboard coherent microwave radar are analyzed and explained. By comparing the simulation with the shipboard radar-measured data, the correctness of the model is verified. The variation of radar echoes with the azimuthal angle between the radar look direction and the dominant wave, the forward motion of the ship, the azimuthal angle between the radar look direction and the forward direction of the ship, and sea states are revealed. This model could provide a theoretical basis for improving the performance of ocean wave measurements on shipboard coherent microwave radar and advancing the microwave ocean remote sensing technique.

Shipboard coherent microwave radar has been a rapidly developing tool for ocean wave measurements. However, the radial velocity estimated from echoes collected with a shipboard radar is significantly affected by the forward speed and six-degrees-of-freedom (six-DOF) motion of the ship. Accordingly, the performance of ocean wave parameter inversion using such radar degrades. To address this problem, the distribution of energy modulated by the ship motion in the wavenumber-frequency spectrum is illustrated, and wave parameters inversion method based on shipboard coherent S-band radar is proposed. An adaptive filter is designed according to the distribution of the energy components modulated by the ship motion in the wavenumber-frequency spectrum. The proposed method filters out the non-wave components and preserves the wave field components. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber-frequency spectrum to obtain the spatial-temporal radial velocities. Then the wave height spectrum is estimated from the radial velocities based on the direct relationship between the radial velocity spectrum and the wave height spectrum. Later, the significant wave height and mean wave period can be derived from the wave height spectrum. A dataset collected with a shipboard coherent S-band radar in the South China Sea in December 2020 is analyzed. The ship deployed with the radar sailed around a wave buoy according to a route plan for two days in that experiment. Comparisons between the radar-estimated and buoy-measured measurements in cases of ship motion are conducted. The results indicate that the proposed method can invert wave parameters well.

For coherent microwave radar, the Bragg scattering from the broken-short waves generated after wave breaking usually introduces extra low-frequency components in the estimated wave height spectrum and, thus, leads to inaccurate retrievals of wave parameters, especially the overestimation of wave period. In order to eliminate the impacts of wave breaking, some methods based on the removal of “group line” in the spatial–temporal domain are proposed to estimate wave parameters. However, these methods are not suitable for the case that the spatial–temporal data are not available. To address this problem, a method is proposed to invert wave parameters only from the time-Doppler spectrum. Temporal velocity series are derived from the time-Doppler spectrum in which breaking components are removed. Then, the wave height spectrum from which wave parameters can be obtained is estimated from the velocity series by the direct transform relationship based on the linear wave theory. Without spatial–temporal data, the “group line” can be removed using the proposed method, and the method is validated by simulation. In addition, an approximately 11-day dataset collected with a shore-based coherent S-band radar deployed along the coast of Zhejiang province in China is reanalyzed and used to retrieve significant wave height and mean wave period. Compared with the buoy-measured data, the significant wave heights and mean wave periods retrieved by the proposed method have the root-mean-square differences (RMSDs) of 0.25 m and 0.60 s, respectively, and also have the correlation coefficients (CCs) of 0.94 and 0.81, respectively. The results indicate that the proposed method can invert wave parameters from the time-Doppler spectrum with a reasonable performance.

The peak wave direction is a crucial parameter to quantify the physical characteristics of the ocean waves. For shipboard coherent microwave radar, the radar-illuminated space changes with the movement of the ship, and this fact leads to an unsolved problem for wave direction estimation. To address the issue, an approach is proposed to estimate peak wave direction using shipboard coherent microwave radar. After estimating water particles’ radial velocity and obtaining the peak wave energy from the radar echoes received with antennas, the 180° ambiguity is removed by comparing the variance of the left and right boundaries of sea echoes in the range-Doppler spectrum. The approach is validated using the datasets collected with a shipboard coherent S-band wave radar and a wave buoy in the South China Sea in 2019. The comparison with the wave buoy data shows that the root-mean-square error (RMSE) of peak wave direction is 10.6°. This result indicates that the method can work effectively.

The broadening first-order sea clutters caused by the signals from different directions with various radial current velocities create severe disturbance for target detection using high-frequency surface wave radar (HFSWR). Conventional sea clutter suppression methods tend to remove the sea clutter and target signals when they are mixed in the Doppler spectrum. Based on the characteristics of the target signal and sea clutter in spatial–temporal domain, a new first-order sea clutter suppression method for HFSWR using orthogonal projection is proposed. The proposed method uses the data from multichannels and slow-time domain at the adjacent range cell to construct a covariance matrix, which can be used to obtain the sea clutter subspace by eigendecomposition. Later, original signals are projected onto the sea clutter subspace. Finally, subtract the component of the original signals in the sea clutter subspace from the original signals to achieve the suppression of sea clutter by retaining the target signals. The simulation and experimental results for a single target and multiple targets cases indicate that the proposed method can suppress the first-order sea clutter effectively, which enhances the target detection capacity in the sea clutter zone for HFSWR.

Microwave Doppler radar has been used to make non-contact river flow measurements and offers a high resolution and a wide range. However, discharge estimation with microwave Doppler radar is challenging due to the inability to measure flow velocity below the river surface based on the Doppler shift. To address this problem, a velocity distribution inversion method based on the Reynolds-Averaged Navier-Stokes (RANS) equations is proposed. A classical two-region model which divides the flow into the inner region and the outer region is applied. Velocity at the inner-outer boundary is estimated using surface velocity and the log law is used to estimate velocity distribution of the inner region. Taking velocities at the surface and the inner-outer boundary as boundary conditions, the velocity distribution in the outer region is derived by solving the RANS equations. Discharge is calculated by combining the proposed method with the velocity-area method. The results illustrated that the velocity distribution obtained by the proposed method and measured by an acoustic instrument are in reasonable agreement. Discharge estimated using the obtained velocity distribution has an error less than 10%. The work indicates the potential of microwave Doppler radar to retrieve the river cross section velocity distribution and estimate discharge.

Coherent microwave radar, which is a rapidly emerging tool to sense waves, usually utilizes the orbit velocities extracted from either temporal or spatial radar echoes. The distribution of energy in the wavenumber-frequency spectrum is changed by some nonlinear features, and correspondingly, this kind of method always provides a significantly overestimated wave period. To address this problem, a novel inversion algorithm, which utilizes the spatial and temporal returns collected with a recently developed coherent S-band radar, is proposed for retrieving wave parameters. A 2-D Fourier transform is applied to the spatial-temporal matrix of velocities to estimate the wavenumber-frequency spectrum. Then the wavenumber-frequency spectrum is integrated over the wavenumber domain to obtain the 1-D velocity spectrum. And the wave height spectrum is estimated from the 1-D velocity spectrum by the direct transform relationship between the 1-D velocity spectrum and the wave height spectrum. Later, significant wave heights and mean wave periods can be derived by the zeroth and first moments of the wave height spectra, respectively. The algorithm is validated using simulation and real data. An approximately four-day dataset collected with a shore-based coherent S-band radar deployed at Beishuang island during a typhoon period is reanalyzed and used to retrieve significant wave heights and mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted, and radar-derived and buoy-measured wave parameters are in a reasonable agreement with a coherent coefficient over 0.9. The results indicate that the proposed method is effective for wave measurements using coherent S-band radar.

A small-aperture receiving array occupying a small area provides convenience for the layout of HF radars. However, this kind of antennas has a wide beam, which adversely affects wave measurements. To solve this problem, a method for extracting wave parameters from wide-beam radar echoes, that models current variations across the beam, is proposed in this article. The nondirectional wave spectrum is extracted from the echoes of a small-aperture HF radar and wave parameters such as the significant wave height, the peak and mean wave period are then calculated from it. Simulation is carried out to assess the performance of the method for different current velocities, radar frequencies and wind conditions. The proposed method is then applied to a 9-day data set collected by an MHF-C radar for further validation via comparisons between the radar-estimated and the buoy-measured wave parameters. Some factors that may affect the performance of wave height measurements are analyzed, such as the signal-to-noise ratio and radial current velocity. The correlation coefficient (CC) between the radar-estimated and the buoy-measured significant wave height is 0.90, and the root mean square difference (RMSD) is 0.51 m. For the mean wave period, the CC is 0.61 and the RMSD is 0.67 s. The results demonstrate that the significant wave height and the mean wave period extracted by the proposed method are consistent with the buoy-measured values and the proposed method can overcome the broadening influence of the Doppler spectrum caused by ocean currents.

Wind speed inversion is a challenging work in the field of ocean surface remote sensing with high-frequency (HF) radars. Recently, the wind speed inversion method based on the first-order HF radar sea echoes attracts much attention. However, most methods, which use radar data at a fixed operating frequency, provide a limited range for wind speed measurement. To overcome the drawback, a wind speed inversion method based on radar data collected with a multifrequency HF radar is proposed. This new method first fits the relationship between the wind speed and the power of the broad-beam first-order HF radar sea echoes and then combines the fit models with the multifrequency HF radar data to estimate wind speed. The proposed method makes use of the information contained in the first-order sea echoes of various operating frequencies so as to improve the performance of the wind speed measurements. Finally, a comparison between radar-estimated and anemometer-measured wind speeds is made to validate the proposed method. Compared with the anemometer data, the wind speeds estimated by the proposed method have a root-mean-square error (RMSE) of 2.27 m/s.

Coherent microwave radar processes sea echoes by using a direct relationship between wave orbital velocity and wave spectrum instead of backscattered intensity to obtain wave parameter. However, the measurements of wave orbital velocity are susceptible to ocean conditions (e.g., low sea state and interference), and thus the method’s performance is often uneven under the mixed conditions. To solve this problem, a novel method for wave parameter inversion based on spectral proper orthogonal decomposition (SPOD) is proposed. The spatial–temporal series of the wave orbital velocities are first processed using SPOD, and a series of expansion coefficients, modes, and eigenvalues can be obtained. Later, the ocean conditions are classified according to the distribution of the eigenvalues, and the truncated frequencies and modes are used to remove nonwave contributions. Finally, wave parameter inversion is performed using the reconstructed spatial–temporal velocities. The proposed method is validated by data from two ocean observation experiments, including coherent S-band radar wave echoes and buoy measurements. The results indicate that the proposed method can discard nonwave contribution and obtain wave parameters in good agreement with buoy measurements regardless of low sea state or mixed ocean conditions.

Coherent microwave radar is one of the effective devices for ocean wave measurements. However, the accuracy of wave inversion is significantly affected by the non-gravity wave components (referred to as non-wave components in this work) such as broken waves and shadows in the sea echoes. In this article, a new method based on the Radon transform and discrete Fourier transform (DFT) is proposed to suppress the non-wave components. First, the spatial-temporal velocities are calculated based on the raw data obtained from coherent microwave radar. Second, the Radon transform and DFT are performed on the spatial-temporal velocities to obtain the distribution of the spatial-temporal velocities in the Radon-Fourier transform domain. Following this, non-wave components are eliminated in accordance with the phase velocity and frequency distribution of the wave components. To validate the method, we analyzed the experimental data collected with a coherent S-band radar in Weihai in December 2021. After removing the non-wave components from the radar echoes, we invert three wave parameters including peak wave period, peak wave direction, and significant wave height, and compare them with thein-situdata. The results indicate that the method can effectively remove the non-wave components from the wave echoes without any empirical parameters.

Coherent microwave radar can provide reasonable wave heights, while wave periods are always overestimated. The “group-line” in the wavenumber–frequency spectrum is a possible mechanism to interpret the overestimation, but the previous model cannot fully explain it. To address the issue, a new model integrating with the breaking and its evolution is proposed for microwave composite sea surface scattering. The upwind-grazing Doppler characteristics for coherent microwave radar under VV polarization are analyzed. The model is validated by comparing the results between the simulation and the radar observation. It explains the creation of “group-lines” in the wavenumber–frequency spectra and indicates that the short waves generated after breaking and their evolution should be the dominant reason for the overestimation of wave period using coherent microwave radar. The result can provide a theoretical basis for improving the performance of wave measurements with coherent microwave radar and for microwave ocean remote sensing.

Deterministic sea wave prediction (DSWP), which aims to predict instantaneous elevation of the ocean surface, can facilitate the safe implementation of many wave height limited maritime operations. Coherent microwave radar is an emerging tool for accurate wave measurements and could be utilized for the advance of DSWP. To address this issue, a method is proposed to estimate the phase-resolved model coefficients and predict the instantaneous wave elevation using coherent microwave radar. The least square with regularization is adopted to estimate phase-resolved model coefficients from radial velocities of water particles extracted from radar echoes. Then instantaneous wave elevation is predicted according to the linear wave propagation model in terms of phase-resolved model coefficients. Simulation analysis is carried out to evaluate the performance of the proposed method under long-crested and short-crested sea states. Subsequently, the proposed method is validated using the dataset collected with a coherent S-band radar. The radar-measured wave spectrum is consistent with the WaveRider data. A comparison between the predicted wave elevation at the location of ship and the phase-shifted roll angle are conducted, and the trends are in an agreement with a correlation coefficient 0.69. In addition, the significant wave height derived from the predicted wave elevation is close to that of the buoy. These results indicate that the proposed method is effective for DSWP using coherent microwave radar.

HF radars have been extensively used for current observation. However, wave measurement with HF radars is challenging mainly due to the limited measurable range of wave height using a single operating frequency. In order to obtain robust wave measurements in complex and various sea states, a wave inversion method is proposed for a multifrequency HF radar. In this method, the nondirectional wave spectrum is directly retrieved from the radar echoes collected at various frequencies (up to four), and then, the significant wave height and the mean wave period are obtained from the integration of the derived nondirectional wave spectrum. Simulation analysis is carried out to evaluate the performance of the proposed method for a case in four various radar frequencies. Then, the proposed method is applied to a three-day observation to validate its advantages by comparing the radar-estimated and WaveRider-measured nondirectional wave spectra. In addition, a 14-day dataset collected with an HF radar operating at 8.267 and 19.2 MHz during a super Typhoon event is selected for further validation via comparisons between the radar-estimated and the buoy-measured wave parameters. The results indicate that the agreement between them is reasonable, and the comparisons also demonstrate that the accuracy of the wave measurement using the proposed multifrequency method is better than that with a single frequency.

High-frequency (HF) ground-wave radars employing compact antenna arrays are advantageous in practical applications because of their convenient deployment. However, the direction-of-arrival (DOA) estimation accuracy may significantly degrade in the presence of array errors for compact phased-array HF radars. Consequently, wind, wave, and current measurements would become unavailable for further applications. In this letter, an array calibration method is proposed for an HF radar system based on a circular antenna array. First, an array manifold model containing array errors is established. Second, a calibrated array manifold is obtained by fitting and interpolating the responses of ship echoes using a least squares fitting method. Finally, the calibrated array manifold and the noise subspace matrix are substituted into the multiple signal classification (MUSIC) method to estimate the DOA of each ship echo. The results indicate that this calibration method significantly reduces the DOA estimation errors with the root-mean-square errors of less than 10°.

A common source of interference in high-frequency (HF) surface wave (HFSW) radars is radio frequency interference (RFI). Its existence inhibits the detection performance of HFSW radars since its amplitude can mask the sea echoes. On the basis of the analysis of RFI characteristics, a new RFI mitigation algorithm based on inverse temporal windowing and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, echoes containing RFI are decomposed into a number of intrinsic mode functions (IMFs) via CEMD and then the inverse temporal windowing technique is applied to each IMFs in the time domain. Test results show that the proposed method outperforms the conventional method in simulated and practical conditions and can effectively mitigate RFI without losing sea echoes.

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.

Compact high-frequency (HF) antenna arrays are convenient to deploy. However, using a small-aperture HF surface wave radar for wind direction measurement is still a challenging problem, since an unsatisfactory array pattern degrades the performance of Bragg ratio estimation. To address this issue, a digital beamforming method based on a superdirective synthesis technique for an HF receiving array that consists of seven elements positioned on a 5-m diameter circle is proposed. This superdirective beamforming method contains a sidelobe constraint. Subsequently, a hybrid superdirective beamforming and direction-finding method is adopted to estimate the wind direction using a multifrequency HF radar based on a circular array (MHF-C). The superdirective beamforming approach, as well as the wind direction estimation method, is presented in detail. The wind direction estimation method has been applied to the raw data sets that were collected with two MHF-C radars installed along the coast of the East China Sea in April 2015 and comparisons between radar-derived and in situ wind directions have been made. Ship-mounted anemometers were used to obtain in situ measurements at six sampling locations within the overlapping coverage of both radars. Another comparison between the radar-derived and anemometer-derived wind directions, which were obtained from June 15, 2015 to August 12, 2015, has also been made. The results indicate that the proposed method is effective for wind direction estimation with root-mean-square differences (RMSDs) between 24.1° and 33.1°, when wind speeds were higher than 5 m/s. The analysis encourages us to recommend a minimum wind speed of 5 m/s for reasonably assessing wind direction measurement performance.

Shore-based coherent microwave radar and high-frequency (HF) surface wave radar are two components of a rapidly emerging set of technologies suitable for ocean wave remote sensing. To investigate their differences, this paper describes and analyzes the relationship between the water particle velocity and the wave height spectrum based on linear wave theory which underpins the algorithms developed for the analysis of data collected by coherent microwave radar. The backscatter mechanism which addresses the interaction of the HF radio waves with the ocean surface waves, as well as the empirical method adopted in our HF radar is also presented. The wave characteristics observed by the shore-based coherent S-band radar [Microwave Ocean Remote SEnsor (MORSE)] are analyzed. A multifrequency HF (MHF) radar based on a circular receiving array, which is capable of sensing waves up to 100-km offshore, is also introduced. An intercomparison of the wave height measurements obtained from the MORSE, MHF radar, and wave buoy is made. The comparison indicates that the wave heights measured by the MORSE and the MHF radar are consistent with the buoy-derived wave heights, with the root-mean-square differences (RMSDs) of 0.27 and 0.37 m, respectively.

Radio frequency interference (RFI) is a common source of interference for high-frequency surface wave radar (HFSWR) since the HF band is shared among many radio services. The existence of RFI greatly degrades the detection performance of HFSWR. On the basis of a detailed analysis of the characteristics of RFI, a new RFI mitigation method based on joint fractional Fourier transform (FRFT) and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, the optimum transform order of the FRFT is obtained through a two-level peak search. Subsequently, echoes that contain RFI are transformed into the fractional Fourier domain and are then decomposed into a number of intrinsic mode functions (IMFs) via CEMD. The RFI is mitigated by implementing detection and localization in each IMF. The reconstructed signal is then transformed into the time domain via an inverse FRFT, and the mitigated signal is finally obtained. Processing results using experimental data indicate that the proposed method can effectively suppress RFI without losing echoes.

The characteristics of radio frequency interference (RFI) are analyzed for high-frequency surface wave radar (HFSWR), which adopts a linear frequency-modulated interrupted continuous wave. If RFI enters the receiver, it contaminates the positive and negative frequency range bins simultaneously, whereas the negative frequency range bins contain the RFI and noise only. Moreover, RFI is always spatially structured over a short duration. Based on the above characteristics, a new orthogonal projection filtering algorithm is proposed for RFI cancelation. The proposed method uses the information of antenna channel and sweep dimensions in negative frequency range bins to estimate the interference covariance matrix. To suppress RFI, the signals of interest in the positive range bins are projected onto the interference subspace, which is obtained by eigendecomposition of the covariance matrix. Experimental results suggest that the proposed method can achieve effective RFI cancelation by using data collected by a multifrequency HFSWR.

Recent studies indicate that the poor antenna sidelobe level of phased-array high-frequency (HF) radars degrades the performance of Bragg ratio estimation for wind direction measurements. To explore this issue, this paper improves the previous model of wind direction estimation for phased-array HF radars, and the effect of unsatisfactory array patterns on Bragg ratio estimation is theoretically analyzed. Moreover, a hybrid beam-forming and direction-finding method, in which wind direction is measured by reducing the influence of unsatisfactory array patterns on Bragg ratio estimation, is proposed for multifrequency HF radars installed along the coast of Zhejiang Province in China. The procedure for implementing this scheme is also presented in detail. Wind directions derived from 8.05-, 10.7-, 16.8-, and 19.2-MHz radar data are compared with measurements collected with anemometers at three sampling locations to verify the proposed method, and the measurements with root-mean-square errors from 19.5° to 26.6° show that this strategy is effective for wind direction measurements. We recommend adopting the proposed method or using similar approaches to estimate wind direction based on HF radars, especially in situations where the array pattern is not sufficiently narrow.

This letter presents a novel signal processing method for HF surface wave radar that offers a solution to suppress significant sea clutter interference. Conventional clutter rejection methods, when the target signal and first-order sea clutter are mixed in the Doppler spectrum, tend to eliminate both the target signal and clutter without distinction. To address this problem, the frequency-modulated difference between the target signal and clutter is considered. Target echoes can be characterized as a linear-frequency modulation signal in long coherent time, while first-order sea clutter can be regarded as a superposition of several single-frequency signals. Based on the hybrid use of singular value decomposition (SVD) and the fractional Fourier transform (FRFT), SVD-FRFT filtering is conducted to suppress the components of the first-order sea clutter without impairing the target signals. The effectiveness of this method is illustrated by simulation and experimental results. It is further demonstrated that SVD-FRFT filtering can improve the signal-to-clutter ratio (SCR) to up to 40 dB for different SCRs.

This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial-temporal joint estimation has been proposed. However, because of the lack of spatial-temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods.

Radio frequency interference (RFI) is a common interference source in high-frequency surface wave (HFSW) radar. Its existence degrades the performance of HFSW radar greatly and makes it necessary to find an effective method to mitigate the interferences. There are two kinds of RFI in the experimental data. One is transient RFI, which is usually suppressed by temporal processing. The other is nontransient RFI, which is suppressed by adaptive beamforming methods. However, the temporal processing techniques suffer performance loss in nontransient cases, whereas the adaptive beamforming methods need spatially structuring, which is difficult to meet within the coherent integration time (CIT) of a few minutes. The fact that the experimental data are usually interfered by two kinds of RFI over a CIT motivates us to find a unified method for interference mitigation. In this letter, a new method based on complex empirical mode decomposition (CEMD) is proposed. CEMD is a local decomposition algorithm that can decompose the echoes and the RFI including transient and nontransient RFI into different intrinsic mode functions (IMFs). Then, the IMFs that correspond to RFI are processed. Experimental results indicate that the proposed method can effectively mitigate both kinds of RFI, and improve the signal-to-noise ratio without losing echoes.

High-frequency radar is an effective instrument for ocean state surveillance. However, phase response differences in antenna elements, cables and receivers cause distortions in beam pattern, resulting in the loss of azimuth resolution. In this letter, a novel method is presented to estimate phase errors in array received data using single-direction-of-arrival (DOA) first-order sea echoes as calibration sources. Eigenstructure decomposition of each signal is used to form a cost function. By minimizing the cost function, phase errors and DOAs are jointly estimated through an iterative procedure. For large phase errors situation, a conditional optimization is conducted to provide rough initial estimates for the iterative procedure. Simulation results reveal the effectiveness of proposed method. We apply this method to estimate the phase errors in the experiment data of multifrequency high-frequency radar. Experiment results validate its performance on DOA estimation.