The digital radio mondiale (DRM)-based passive radar (PR) has now attracted wide attention for long-range aircraft detection. Use of long coherent integration time is an efficient way to compensate the large attenuation when the PR works via skywave propagation, but it also introduces nonstationarity into the echo signals due to target maneuver and ionospheric channel variation. This nonstationarity will severely degrade the performance of conventional constant false alarm rate (CFAR) detectors. To account for this problem, a novel processing scheme is proposed to improve the nonstationary target detection on the time-frequency (TF) plane. First, the TF multisqueezing transform (TFMST) is used to catch the TF signature and address energy divergence in the TF representation due to the ionospheric propagation. Next, morphological methodologies are applied to determine the regions containing potential targets. Finally, for each potential target, the duration, velocity, and energy are checked to decide whether it is a target. Simulation results show that, the detection probability of the proposed method is significantly increased compared with that of the existing methods for a low signal-to-clutter-and-noise ratio (SCNR). When tested on a dataset collected in a PR experiment with a 1150 km far DRM illuminator, the proposed method is able to detect targets up to 125 km that cannot be detected by those existing methods, which further proves its superiority for nonstationary target detection.

Noncontact flow measurement methods have been popular among hydrographers for their convenient and safe operation in recent years. Ultrahigh frequency (UHF) radar is an essential flow measurement instrument that is normally deployed on the river bank to monitor flow velocity and discharge. However, when the river flow is too high, some frequency points of the Doppler spectrum are aliased. The frequency point in the aliased first-order peak region may contain more than one current signal, which cannot be distinguished by the previous radial current extraction methods. The consequent poor radial current decreases the estimation performance of cross-sectional surface velocity and river discharge. This letter first interprets the mechanism of the aliasing phenomenon according to the relationship between Bragg frequency and Doppler shift. Then, a radial current extraction method based on the classification of Doppler frequency points is proposed to solve the aliasing problem. Experimental results demonstrate that the proposed method outperforms the other two radial current extraction methods in calculating discharge with a reduction in a root-mean-square error (RMSE) of 364 and$574 \mathrm {m^{3}/s}$, respectively.

A high-frequency (HF) surface-wave radar has been widely used in marine monitoring and vessel surveillance, among which compact ones, e.g., a crossed-loop/monopole antenna (CMA), are preferred in many scenarios for their low costs and easy maintenance. Relatively large error in the direction of arrival (DOA) estimation due to the small aperture involved is one major problem in such compact radars. To solve this problem, a biologically inspired DOA estimation method is proposed to improve the direction finding (DF) performance. The phase amplification mechanism of Ormia ochracea is imitated with a digital signal processor appended to the radar receiver. Then, the multiple signal classification (MUSIC) algorithm is used to estimate the DOA. For a two-element array with a small spacing, simulation results show that the proposed Bio-MUSIC method is superior to the conventional MUSIC in DOA estimation, and the combination of cosine and sine loops offers significant advantages over that by using two monopoles in terms of both accuracy and stability over different look angles. Also, compared with the CMA, the root-mean-square error (RMSE) is reduced by up to 56.99%. Experimental results demonstrate that, the total RMSEs of the ship coordinates obtained by Bio-MUSIC based on two cosine loops or one cosine loop plus one sine loop are reduced by up to 41.23% and are much less than the RMSEs obtained by MUSIC based on the CMA in most cases. Consequently, the biomimetic antenna array (BMAA) of loops with Bio-MUSIC can also be a good choice for marine applications.

River discharge is an essential hydrological index of the global water cycle and is important for flood and drought forecast. Ultrahigh frequency (UHF) radar has gained much attention since it can remotely retrieve river discharge from the surface flow velocity measurement in real-time and all-weather condition. However, it becomes challenging for the mountain river case where the water level changes more significant than that of the plain river due to the deep-narrow channel, which breaks the previous empirical relationship between surface flow velocity and discharge. In this letter, an improved index-velocity method is proposed to address this issue. Through modeling the nonuniform variation of the cross-sectional area with water level, a more accurate surface flow velocity–discharge relationship is established. Experimental results show that the proposed method produces higher calculation accuracy than that of the previous methods, which enlarges the applicable scenario of UHF radar in discharge measurement.

Compact high-frequency (HF) radar is a promising tool for maritime surveillance owing to its over-the-horizon and all-weather ability. However, it typically suffers from low tracking accuracy due to limited azimuth resolution. Cross localization using multiple radars provides a solution, but track-to-track association among radars remains a critical challenge. Previous association methods are mainly subject to that each radar’s range and azimuth measurement errors are comparable and sufficiently small, which is not satisfied in HF radar. In this article, we propose a novel track association algorithm based on the target region match (TRM) strategy, which employs the joint range and azimuth gates intersection criterion to combine the effect of range and azimuth measurement errors. Considering the azimuth error is much larger than that of the range, their contribution to the track association is balanced through the area intersection operation. Both simulation and experiment results show that the proposed TRM method produces better association performance than the other investigated methods.

Nearshore wind speed information is important for many research and commercial applications. Shore-based ultrahigh-frequency (UHF) radars promise all-weather, real-time, high temporal and spatial resolution measurements of nearshore wind. Until now, there are no reports on the method of measuring wind speed using the shore-based UHF radar. In this article, a method of wind speed extraction from the Bragg peak width of Doppler spectrum is proposed. Two models describing the relationship between the Bragg peak width and wind speed and direction are developed through theoretical derivation and numerical simulation. One is a combination of logarithmic functions and the other is a combination of power and logarithmic functions. The constants in these models are determined through data fitting. In the inversion process, the wind direction is first estimated, and then the wind direction and the Bragg peak width are substituted into the models to find the wind speed. Using the anemometer data of a buoy as the ground truth, it is proved that the method is feasible and the second model has better performance with a RMSE of 1m/s.

In addition to sea state monitoring, high-frequency surface wave radar (HFSWR) has attracted more and more attention in ship detection and tracking. Constant false alarm rate (CFAR) detectors have been widely used to handle the complex properties of sea clutters. Due to the relatively high detection threshold, the performance of CFAR in detecting weak and nonstationary targets is often not good. To address this problem, time–frequency analysis (TFA) is involved in the ship detection and direction finding (DF) processing. First, the probability distribution model of sea clutter is achieved in the time–frequency (TF) domain. Corresponding to this sea clutter model, the decision thresholds of targets on the TF plane under different false alarm rates$P_{\mathrm {fa}}$are calculated. Next, the array snapshots are formed by the spectral samples along each extracted TF ridge and later used in the DF process to give the direction of arrival (DOA). Experimental results show that, with the automatic identification system (AIS) records as the ground truth, the number of matched targets detected by the proposed TF-CFAR method is 5%–8% greater than that by the cell averaging (CA)-CFAR method. Moreover, the TF multiple-signal classification (MUSIC) also outperforms MUSIC with an improvement of 3.52° in the root-mean-square error (RMSE) of the DOA estimates under a low signal-to-noise ratio (SNR). In conclusion, the involvement of TFA can greatly improve the detection and DF performances of compact HF radar, particularly under the situations of low SNR and target nonstationarity.

Shore-based Ultra-high-frequency (UHF) radars have similar characteristics as the High-frequency (HF) ocean radars, for example, the sea waves generating Bragg resonance are gravity waves. Compared to HF radars, UHF radars are more sensitive to the small changes of sea waves, have finer spatial resolution and measure sea areas closer to the coast, hence are expected to complement the nearshore sensing capacity of HF radars. Two wind direction inversion methods for coastal UHF radars are adopted. One is based on the Bragg peak power ratio and the other is based on the modified Doppler spectrum centroid. The latter is proposed to address the problem that it is often difficult to calculate the Bragg peak power ratio at high wind speeds. In both methods, several models describing the relationship between the Doppler spectrum characteristics and wind direction are used respectively in combination with the least square method to estimate the wind direction with the ambiguity removed. Using the buoy wind sensor data as the ground truth, the results of a three-week experiment demonstrate the feasibility of the two methods. It is also shown that the former method has slightly smaller errors and the latter method applies to a wider range of wind speeds. For both methods, the most recommended model is the one based on the cosine-type directional spreading function with variable spreading parameter, because it has small errors and does not require data fitting.

The ocean-wave-height-measurement accuracy and coverage of high-frequency (HF) radars are affected by the echo intensity. Due to the effects of propagation attenuation and the wind-direction spreading function, the same sea area will generate different echo intensities for radars at different locations. Based on this characteristic, a new wave-height-measurement method combining multiple HF radars is proposed in this letter. First, the advantages of the radar network in enhancing the coverage and antiinterference performance of wave-height measurement are analyzed quantitatively. Furthermore, it is found that the signal-to-noise ratio of the second-order echo can be used as weights to fuse the wave heights measured by different radars. A field experiment over 23 days shows that the proposed fusion algorithm effectively improves the radar coverage and the wave-height-measurement accuracy.

High frequency (HF) radar often indirectly inverts wind field from wave height field, so the accuracy, range, and spatial resolution of the estimated wind field are usually limited by the wave field estimation performance. However, the sea surface current including the wind-driven current can often be accurately measured by radar. Thus, based on the wind-driven current estimated by HF radar, a new wind speed inversion algorithm is proposed in this letter. First, the relationship between wind speed and wind-driven current speed is established according to the ocean dynamics theory, but it contains several uncertain parameters. To avoid solving these parameters, an artificial neural network is used to train an accurate model between wind-driven current speed and wind speed. Final, the wind-driven current estimated by radar is substituted into the model to extract wind speed. A field experiment shows that the average correlation coefficient between the radar-estimated wind speed and the reference wind speed is 0.86, and the average root mean square error is 2.38 m/s. In addition, the proposed algorithm has larger measurement range and better spatial resolution than traditional algorithms.

Based on the sea surface scattering mechanism of radio wave, compact high-frequency surface wave radar that employs the direction finding technology also shows promising potential for remotely mapping of ocean wave parameters. However, due to the low signal-to-noise ratio (SNR) of scattered echoes, the diverse external interference and clutter signals, other unresolved measurement uncertainties, the quality (such as accuracy, temporal, and spatial coverage rates) of wave maps are often limited. In this article, a novel, real-time, data quality control method is proposed to alleviate these issues. A comprehensive three-stage processing scheme is established, including the range-Doppler spectral processing, the spatial-grid processing, and the temporal-scale Kalman filtering. The first two stages aim to improve the echo signal quality and reduce the spatial gaps, respectively. The third stage is designed to mitigate the estimation error using an autoregression prediction model and to relate the observation error variance to the SNR of second-order Doppler spectral peak. A detailed verification and performance analysis between the field radar data andin situground truth data over one-month period is carried out, indicating that the proposed method can improve the reliability of wave maps with respect to the conventional Doppler spectral smoothing or averaging method, particularly in low sea state (i.e., low SNR) scenarios.

The direction-finding high-frequency (HF) radar that employs a crossed-loop/monopole antenna as receiving has been widely applied in sea surface dynamics remote sensing due to its compact footprint and remarkable performance. Nevertheless, the direction-of-arrival (DOA) estimation of this system remains challenging in the case of coherent signals, which imposes a great threat to the radar measurement accuracy but has rarely been concerned in the past. In this article, a novel method combining the covariance matrix reconstruction process and subspace estimation technology is proposed to deal with this problem. By exploiting the purely real property of the array manifold, the rank loss of the covariance matrix due to the coherence of signals is mitigated. Both the theoretical derivation and numerical analysis prove that the proposed method is valid in estimating two coherent signals, and the DOA estimation accuracy is mainly related to the signal-to-noise ratio (SNR) and angle separation of the signals. Finally, field experiment data are also used to validate the effectiveness of the proposed method.

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 surface wave radar has been widely applied to the measurement of sea surface current, but its accuracy and direction resolution of wave parameter estimation are always limited due to the wide beam of the antenna. In this article, a novel wave-height mapping method based on the second-order harmonic peak (SHP) of radar Doppler spectra is proposed to address this concern. The characteristic of the SHP at the Doppler frequency of$\sqrt {2}$times the Bragg frequency is studied through the theoretical derivation and numerical simulation. A relationship between the ratio ($R$) of the SHP power to the Bragg peak power and significant wave height ($H_{s}$) is derived. Furthermore, the$R$–$H_{s}$model is improved by incorporating influences, such as background noise and antenna beamwidth. With this improved model, a wave-height mapping algorithm based on the direction finding technique is presented. This approach enables the significant wave-height map extraction using a broad-beam radar. Finally, wave-height maps obtained at different sea states are depicted and analyzed, and the wave heights appearing on the maps are compared with buoy data over a one-month experiment to verify the validity and robustness of the algorithm. During this period, the significant wave height varies from about 0.5 to 4.5 m, and the radar measured wave heights at different range/distance bins show an overall root-mean-square error (RMSE) of 0.33–0.77 m and a correlation coefficient (CC) of 0.78–0.94, with respect to the buoy measurements.

Ocean wave height measurement using a wide-beam high-frequency surface wave radar (HFSWR) remains challenging due to its poor spatial resolution, which significantly limits the application of such compact systems. In this article, a novel method for wave height field extraction from the first-order Doppler spectra of a dual-frequency wide-beam radar is proposed. A model relating significant wave height to the ratio of the first-order spectral powers associated with two radar frequencies is put forward and studied numerically. Through theoretical analysis and experimental validation, it is confirmed that the first-order Doppler peaks of two radar frequencies have arisen from an approximately same direction of arrival (DOA), and their amplitudes are also affected by a similar wave directional spreading. Hereby, an algorithm combining beamforming and direction finding is developed to determine the spatial distribution of the first-order spectral power ratio and derive the significant wave height field. Finally, experimental results are given to verify the algorithm. The radar-derived wave height field agrees well with that obtained using a numerical wave model. Furthermore, the radar-measured wave heights are compared with the data collected by two buoys at the distances of 12.7 and 73 km, respectively. The comparison shows that the corresponding root-mean-square errors are 0.3 and 0.5 m and the correlation coefficients are 0.85 and 0.88, respectively.

Attributed to the maneuverability, the shipborne high-frequency surface wave radar (HFSWR) enables a larger coverage in ocean wind measurement than a shored-based system does. However, the antenna aperture of shipborne radar is usually limited by the ship size, which troubles the direction of arrival (DOA) estimation accuracy. In addition, the wind direction ambiguity caused by monostatic radar illumination remains challenging. In this article, an unambiguous wind direction estimation method based on a compact shipborne HFSWR is proposed. An equivalent dual-station model combining the successive radar data collected at two close locations is developed to solve the DOA ambiguity in wind direction estimation. A direction-finding algorithm incorporating the Doppler shifts due to ship motion and current shear is adopted to estimate wind direction from the ratio of the positive and negative Bragg peak powers. Moreover, three different wave directional spreading models are applied for performance comparison. Numerical simulation is conducted to evaluate the effect of dual-station configuration on the estimation accuracy. Finally, field experimental results are given to verify the correctness of the method. Radar-derived wind direction field agrees well with that provided by a numerical weather model.

The shipborne high-frequency ground wave radar (HFGWR) provides a better condition for sea surface target detection than the shore-based one because of its maneuverability and flexibility. However, the first-order Bragg spectra of the shipborne HFGWR would be broadened by the Doppler shift of the ship motion, which makes the vessel echo easily submerged. Even though some methods have been applied to suppress the broadened Bragg spectra for target detection, most of them are hard to achieve good result in a small-aperture radar. An orthogonal projection method to suppress the broadened Bragg spectra using a cross-loop/monopole array is proposed in this letter. The proposed method mainly includes two steps: 1) estimate the space of sea clutter in a spatial domain according to the ocean echo characteristics and 2) design an adaptive filter of single or multiple Doppler bins on the estimated clutter space to suppress the first-order spectra. After suppression, we use the multiple signal classification algorithm to estimate the incident direction of targets. The experimental data validate that the sea clutter is well suppressed and the target signal-to-clutter ratio is increased by the algorithm.

High-frequency (HF) ground wave radar is an important tool for sea state measurement and low-altitude target detection. Dense radio frequency interference (RFI) in the HF band inhibits the extraction of sea state parameters and degrades the performance of radar. Though many interference suppression methods based on large arrays have been proposed, most of them are weak to deal with the interference in small-aperture radar. In this letter, a spatial subspace method is proposed to suppress RFI, which uses two crossed-loop/monopole antennas to construct interference subspace at the reserved range bins and projects the echoes onto its orthogonal subspace at the interested range bins. The processed results of measured data from OSMAR-SD verify the high performance of this algorithm for dense RFI suppression. Apart from that, the data utilization ratio and the accuracy of estimating wave height are both improved significantly after the RFI suppression.

A compact-array high-frequency surface wave radar equipped with two crossed-loop/monopole receiving antennas has been established for vessel detection. Using two compact antennas of the same design, this system can obtain two extremely similar sets of radar range-Doppler spectra over the same period. To detect vessel targets efficiently, the spectra of two antennas are enhanced by performing a principle component analysis. A wavelet-based approach is then applied to suppress clutter and reduce noise. The signal-to-noise ratios and signal-to-clutter ratios of the echoes are thus improved. Finally, an adaptive threshold is used to extract targets. The real radar data detection results are compared with Automatic Identification System data as well as those from the conventional ordered-statistic constant false alarm rate method. The feasibility and the validity of method proposed here are thus demonstrated.

In addition to being able to extract current velocities and wind directions, the first-order Bragg peaks in high-frequency (HF) radar sea echoes also have the potential to independently map wind speeds. During a two-month experiment with the HF-radar ocean state measuring and analyzing radar, model S in the Taiwan Strait in 2013, the dependence of the first-order spectral power (FSP) on wind was found to be deterministic. An empirical model was thereafter proposed to estimate the wind speed, which gave a performance that was comparable with the popular second-order inversion method. Strong oscillations (up to ±3 dB) in the FSP caused by underlying currents were observed and cannot be readily explained by the classic wave-current interaction theory with the assumption of wave-action conservation. When we matched the local maximum and minimum values of the FSP with the local extreme and zero values of the radial-current velocities, we found that most of the extremes of the FSP occurred when the radial-current velocity was 0. To account for this phenomenon, additional smoothing with a 12.4-h moving window was applied, which led to a significant improvement in the wind-speed estimates. This knowledge of the FSP's wind and current dependence helps us make another step toward the operational estimation of wind speeds using the first-order Bragg peaks.

Waveheight measurements with high-frequency radar have been troubled by the limitation dependent upon radio frequency for decades. It is suggested that high frequencies are needed for measuring low waves, whereas low frequencies are suitable for high waves. In this letter, a new waveheight inversion method using dual frequency is presented. Measurements of the two frequencies are fused together, according to their Doppler spectrum properties, to improve the range and accuracy of waveheight estimation. Experimental results collected on the coast of the Taiwan Strait over 18 days are shown to prove the validity of the method.