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.

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.

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.

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.

Ship detection at the sea surface is important for improving human marine activities. Most existing ship detection methods for high-frequency surface wave radar (HFSWR) are based on peak and constant false alarm rate (CFAR) detection and require a coherent integration time (CIT) of several minutes. However, in such a long period, the target may not be stationary. To account for the nonstationary property, a time-frequency analysis (TFA)-based ship detection and direction finding (DF) method is proposed for HFSWR. Target ridges on the TF representation (TFR) of the echo data are detected first. Next, array snapshots are formed by sampling the extracted ridges and are used to estimate the direction of arrival (DOA). The processing results of the radar data collected at Dongshan, Fujian Province, China, show that the proposed method outperforms the CFAR method with both increased detection rates and decreased DF errors, especially under relatively low signal-to-noise ratio (SNR) scenarios.

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.

River discharge measurement is of great significance, and as such, many methods and measuring instruments have been developed by many hydrologic researchers. However, most commonly used measuring instruments must be in contact with the water and are difficult to place and maintain. In this letter, a noncontact measurement method is proposed using ultrahigh- frequency (UHF) radar. The index-velocity method is employed to calculate the mean cross-sectional velocity, and the water-surface velocities on the cross section detected by the UHF radar are used to select the optimum index velocity. A field experiment was conducted in the Hanjiang River at Xiantao, Hubei, China, from March to July 2018. Data from March to June were processed for selecting the optimum index velocity, and the maximum water-surface velocity of the cross section was proven to be optimal. The fit relationship between the mean cross-sectional velocity and the index velocity was used to estimate the mean cross-sectional velocities and river discharges for June to July. The results were compared with those provided by the Hubei Xiantao hydrologic station, and the errors were mostly within ±5%. This confirms that the UHF radar can be used to accurately, automatically, and continuously measure river discharge.

In this letter, the application of ultra-high frequency (UHF) radar for ship target detection over river is investigated for the first time. Due to the wide beam width of antenna, the ship detection of the UHF radar suffers from the broaden river clutters scattering from water waves. In addition, due to the high resolution, the ship echoes are seriously extended in both range and Doppler dimensions, which is different from the traditional point target signal. Extended target detection in strong river clutter is applied in this letter. Conventional constant false alarm rate (CFAR) detector is limited in this situation, which is applicable to the point target detection in clutter free background. An improved cell-averaging (CA)-CFAR method is proposed based on a joint estimation of threshold in range, Doppler and time serials of range-Doppler (R-D) spectra. Based on the time stationarity of clutter, this three-dimensional (3D) CA-CFAR combines several R-D spectra collected in continuous time to estimate the clutter threshold. The effectiveness of this improved method is validated using both simulated and field data.

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 direction-finding high-frequency radars (HFRs) are widely used for remotely sensing the ocean surface currents. However, the performance of the direction-finding HFR is limited by the inevitable distortion of the actual antenna pattern of the compact monopole-cross-loop antenna, because the distortion of the antenna pattern leads to errors on the direction-of-arrival (DOA) estimation, which results in correctly determined radial velocities being placed into the incorrect bearing sectors. In this letter, a model determining the DOA estimation error (DOAEE) stemming from the antenna pattern distortion (APD) is presented with a detailed analytical derivation. This model suggests that the DOAEE only depends on the relative distortion of the two orthogonal loops. To validate the correctness of the proposed model, simulations are carried out. In addition, the simulation results show that the model well captures the actual relationship between the APD and the DOAEE. Moreover, a data set collected by a direction-finding HFR system and a synchronized automatic identification system receiver is used to further validate the proposed model. The precessing result of this data set demonstrates that the model-calculated DOAEE and the actual DOAEE are in good agreement with a correlation coefficient of 0.92 and a root-mean-square error of 9°, which provides solid evidence to verify the validity of the proposed model in determining the relationship between the DOAEE and the APD.

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.

Wave-current interaction is a common and important phenomenon in the ocean. As an ocean remote sensing tool, high-frequency (HF) radar can be used to measure currents and wave parameters. In this paper, the possibility of studying wave-current interaction using HF radar is investigated. The first-order spectral power (FSP) of HF radar is used to explore the effect of current on the Bragg wave. By analyzing the FSP change with current (FSP-current distribution), we find that, in deep water, the wave-current interactions mainly belong to 2-D refraction case, while, over a relatively shallow shelf, the interactions are stronger and more complicated. Based on local topography and current field data at Taiwan Strait, the simulation results obtained using the SWAN model confirm the 2-D refraction of the Bragg wave. When the wave-current interaction is stable, we compensate the FSP with radar-measured currents according to the radar extracted FSP-current distribution and achieve a more accurate wind estimation. Comparisons between the original and refined wind fields show the effectiveness and necessity of the current-based compensation.

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.

High-frequency (HF) radars have great potential for maritime surveillance, and the multiple signal classification (MUSIC) algorithm is usually used to estimate the direction of arrival (DOA) of targets for a wide-beam radar. However, the performance of the MUSIC algorithm relies on the precision of the antenna pattern, which could be contaminated by nearby electromagnetic interference. Therefore, the actual antenna pattern must be measured and used. In order to remove the requirement of antenna pattern measurement, a new method for target DOA estimation from wide-beam HF radar data using support vector regression (SVR) is proposed in this letter. A system model that relates target bearing and radar data feature is obtained through the SVR-based machine learning using the automatic identification system data and data associated with the vessels successfully detected by the HF radar. Then, such a model is used to determine the DOAs of targets from new data. The field experimental results at two sites demonstrate that the performance of the SVR method is better than that of the MUSIC algorithm.

Wind spreading functions (WSF) are crucial for high-frequency radar (HFR) wind-direction inversion. The popular half-angle cosine WSF always fails to describe observed HFR Doppler spectra and tends to provide almost fixed relative angle estimations. In this letter, analysis of the data from a broad-beam HFR radar, deployed on the Taiwan Strait's west coast, shows that a modified WSF (based on the cosine WSF) has a better wind-direction estimation performance. The modified WSF fits average Bragg ratios of 15-day data well with the aid of data from buoys. The data of the next 13 days are used to test the modified WSF. The wind direction estimated by the modified WSF has an advantage when Bragg ratios have adequate average processing, and directions of arrival are around the upwind or downwind direction. The root mean square error of the modified WSF wind-direction estimate is 32.59° for the entire observation, decreasing to 14.18° when a significant wave height is between 1 and 2 m.

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.

The conventional second-order Bragg-spectrum-based wave height extraction method is often susceptible to external noise and spatial aliasing. To improve the wave height estimate, we turn to the first-order Bragg peaks and propose a new method which directly estimates the wave height from them. The key point is to confirm and use the unsaturated property of the first-order Bragg spectral power. The quantitative relation between the first-order Bragg peak power and the significant wave height can be established with the help of an in situ wave buoy, and consequently, the wave height is to be read out from the curve via the maximum Bragg peak power on one range cell. The first-order method is validated by a two-month-long data set collected by the OSMAR-S radar at 13 MHz. Compared with the second-order method, the improvement is obvious under low and moderate sea states. The new method opens the way for wider use of the first-order Bragg peaks in wave height extraction by high-frequency radars.

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.

High-frequency (HF) radars have achieved great success in the remote sensing of sea states. Most operational methods for wave extraction resort to the second-order Doppler spectral continuum based on Barrick's formulas. However, accurate division of the first-order Bragg peak and the second-order continuum is often unavailable due to spectral broadening and splitting when the radar beam is not sufficiently narrow. The second-order peaks, particularly the second-harmonic peaks (SHPs), can also introduce large errors to the wave height estimates. In this letter, we describe some properties of the SHP that are directly learned from the experimental observations of sea echoes by HF radars in both the 25- and 13-MHz bands. The SHPs exist throughout two radar experiments with comparable strengths to the maximum of the second-order continuum. They have similar spectral structures as the Bragg peaks in both the Doppler and spatial domains, showing that these peaks are representations of some particular sea waves. The power ratios of the SHPs to the Bragg peaks (RSBs) in decibels have strong correlations with the wave heights recorded by an in situ buoy or a known model, and the linear regression models between the RSB and the wave height can be determined by data fitting. These findings give new insights into wave height estimation from the spectral peaks only, which can provide supplemental information to the conventional results from the second-order continuum to improve both the accuracy and robustness of the wave extraction by HF radars.

Ionospheric clutter resulting from undesired radio signals reflected by the ionosphere severely deteriorates the detection performance of the high-frequency surface wave radar (HFSWR) in the mid- and low-latitude regions. To solve this problem, we propose a novel antenna configuration and method, i.e., using vertically placed, coaxial, multilayer crossed-loops as auxiliary antennas to cancel the clutter on the uppermost main crossed-loop (and/or monopole) antenna. The difference signals between the main and the auxiliary loops with the same normal direction are used as the reference signals in the adaptive filtering scheme to avoid cancellation of the wanted signals, i.e., the echoes horizontally coming from the sea surface. Both simulation and some preliminary experimental results are demonstrated to show the validity of the method.

High-frequency (HF) radars have gained much attention in recent years due to their remarkable capabilities in remote sensing of sea surface states. Radio frequency interference (RFI) should be suppressed before extraction of useful information from the radar echoes. Element instead of array-based interference suppression method is preferred for a small-aperture radar, and it is still more difficult to deal with the nonstationary interference. In this letter, a short-time range domain cancellation method for RFI suppression is proposed, whose advantage over the global one is twofolded: better suppression performance and lower computation cost. Processing results of real data collected by OSMAR-S, a portable HF radar, show the validness of the short-time method for nonstationary RFI suppression. The method can greatly improve the anti-interference capability and detection performance of the HF radar.

The paper analyzes the characteristics of cochannel interference (CCI) in the high-frequency (HF) surface wave radar (HFSWR), which adopts the linear frequency modulated interrupted continuous wave (FMICW). CCI will influence all the range bins, including all the positive and negative frequencies, and the negative frequency range bins contain only the external interference information. Based on the above characteristics, we introduce a new adaptive coherent side-lobe cancellation (CSLC) algorithm based on subarrays that use the negative frequency range bin samples to estimate the interference covariance matrix and correlation vector. Experimental results confirm that the general and robust algorithm can achieve effective CCI suppression using the data recorded by the Ocean State Monitor and Analysis Radar (OSMAR2003, manufactured in 2003), located near Zhoushan in Zhejiang, China.

In order to cancel the daytime interference of Ocean State Monitoring and Analyzing Radar, which appears in some consecutive days, we analyze the interference characteristic. Based on the characteristic the sidelobe cancellation technique and the orthogonal expansion method are proposed to cancel the interference. Both methods increase the sea-echo-to-interference ratio, but the latter achieves better effect in the far distance, which confirms the orthogonal expansion is the best method to remove the interference.

Radio frequency interference (RFI) is often a major problem in high-frequency (HF) radar operation, and until now, few existing methods worked very well to suppress dense RFI. In this letter, a theoretical analysis of the range-domain correlation of RFI in HF chirp radar is presented, and the relation between the correlation functions of RFI in range and fast time domain is given; based on this, a new method for RFI suppression is developed. The echo signals at the reserved range bins are used to construct an interference subspace, and then, the signals at the range bins of interest are projected onto its orthogonal subspace to suppress the RFI. When applied to radar signal processing, the method suppressed dense nighttime RFI effectively. It greatly improves the radar's detection performance in dense RFI.

High-frequency (HF) radar can be employed to measure sea surface state parameters such as waveheight, wind field, and surface current velocity. This paper describes the application of the HF ground wave radar in remote sensing the surface conditions over the Eastern China Sea in October 2000. The radar, referred to as the OSMAR2000, was developed by Wuhan University. Preliminary wave spectra, waveheights, and wind fields estimated from the collected data are presented and compared with ship-recorded measurements where such are available. The range for wind direction sensing is up to 200 km. Wave information and wind speed can be provided up to a range of 120 km. The mean difference between radar- and ship-measured significant waveheight is 0.323 m; wind direction is measured within 20/spl deg/; and wind speed to within 0.6 m/s. With such agreement being fairly reasonable, the feasibility of the inversion algorithm and the ocean state real-time sensing capability of OSMAR2000 are demonstrated.