High-frequency Surface Wave Radar System Research Articles

Characteristics of Spring Sea Surface Currents near the Pearl River Estuary Observed by a Three-Station High-Frequency Surface Wave Radar System

The processes of ocean dynamics are complex near the Pearl River Estuary and are not clear due to a lack of abundant observations. The spatial characteristics of the spring sea surface currents in the adjacent waters of the Pearl River Estuary were analyzed using the current data observed by a three-station high-frequency surface wave radar system (HFSWRS). Compared with the two-station HFSWRS, the deviation of current velocity and direction observed by the three-station HFSWRS from the underway measurements decreased by 42.86% and 38.30%, respectively. The analyzed results show that the M2 tidal current is the dominant current among all the tidal constituents, followed by K1, with angles of inclination ranging from 130° to 150°. The tidal flow is dominated by northwest–southeast back-and-forth flow. In the southern part of the observed area, which is far from the coastline, the tidal current ellipses exhibit a circular pattern. The prevalent tidal current type in this region is irregularly semi-diurnal, and the shallow water constituents also have a significant effect. The tidal energy in the adjacent waters of the Pearl River Estuary is affected by potential energy flux and kinetic energy flux. As the water depth and currents velocity increase in the southeast direction, the tidal energy flux increases. In the nearshore zone, the direction of tidal energy flux varies along the coastline. The changes in the residual current within the observed area are correlated with the sea surface wind field. Based on the high-precision sea surface current observed by the three-station HFSWRS, the characteristics of the ocean dynamic processes near the Pearl River Estuary are analyzed comprehensively, which provides important reference and confidence for the application of the developing new radar observing network with about 10 radar stations near the Pearl River Estuary.

Improved Main Lobe Cancellation Method for Suppression Directional Noise in HFSWR Systems

High frequency surface wave radar (HFSWR) has been successfully developed for early warning, especially for vessel target detection. However, the system’s performance is consistently constrained by external environmental noise, particularly directional noise, which presents a new problem for HFSWR. Anisotropic directional noise has complex behavior, and its noise level is generally increased by 10 to 15 dB compared to traditional noise floor level. Suppressing varying directional noise and exploring obscured targets are challenging tasks for HFSWR. In this paper, a novel algorithm based on angle-Doppler joint multi-eigenvector synthesis, which considers the angle-Doppler map of radar echoes, is adopted to analyze the characteristics of the directional noise. Given the measured data set, we first analyze the directional noise-spatial correlation. Then, an algorithm based on sliding main lobe cancellation (SL-MLC) based on a sliding single-notch space filter (SSNSF) is proposed to block target components and get training data that contains precise directional noise information. Finally, the method is examined by measured data, and the results indicate the method has better performance for directional noise than the compared method.

Linear Frequency Modulation and Orthogonal Code Modulation for Co-Located Multiple-Input Multiple-Output High-Frequency Surface Wave Radar

A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input multiple-output (MIMO) technology is an attractive method to improve angular resolution. This paper proposes MIMO waveforms and their processing that can be used in HFSWR systems. This dual modulation method applies linear frequency modulation to each pulse and orthogonal polyphase codes for a few consecutive pulses to enable MIMO processing. The proposed method can effectively remove the correlation of mutual interference and exhibits excellent performance in removing multiple-time-around echoes.

Overview of ionosphere clutter suppression for high frequency surface wave radar (HFSWR) system: Observation, approaches, challenges and open issue

AbstractA great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards.

Space‐time adaptive processing algorithm based on hyper beamforming for ionospheric clutter suppression in small‐array high‐frequency surface wave radar

AbstractSmall‐array high‐frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on‐land resources. In small‐array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal‐to‐clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space‐time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small‐array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF‐JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF‐JDL is still applicable at lower SCRs of the target compared with JDL.

Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

A Self-Regulating Multi-Clutter Suppression Framework for Small Aperture HFSWR Systems

The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods.

A STAP clutter suppression algorithm based on sparse representation for small-array HFSWR

ABSTRACT In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

Direct target localization and vector-velocity measurement method based on bandwidth synthesis in distributed high frequency surface wave radar

The accuracy of target position and velocity measurement is one of the most concerned indexes of Distributed High Frequency Surface Wave Radar (HFSWR). The target localization of HFSWR is commonly achieved by indirect positioning approach. Recently, some direct positioning methods using the raw signals collected on all base-stations have been studied, which have superior positioning performance compared with indirect approaches. But there is almost no joint estimation method for target position and vector-velocity. In this paper, a novel direct target localization and vector-velocity measurement method based on bandwidth synthesis (BS-DTL) in distributed HFSWR system is investigated to improve the accuracy of the target. BS-DTL algorithm that makes full use of the information of all base-stations is considered for the joint estimation of target position and vector-velocity. This paper studies the detection and estimation performance of the BS-DTL algorithm and traditional direct target localization method (DTL). Numerical simulation results show that the proposed algorithm outperforms the traditional DTL method and the position and vector-velocity can be accurately estimated.

An Automatic Target Detection Method Based on Multidirection Dictionary Learning for HFSWR

Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.

Vessel Velocity Estimation and Tracking From Doppler Echoes of T/R-R Composite Compact HFSWR

Vessel speed and heading are two important kinematic parameters describing its state of motion. However, due to low spatial resolution of a compact high-frequency surface wave radar (HFSWR), vessel speed and heading cannot always be accurately estimated. Since HFSWR can measure vessel Doppler velocity with relatively high accuracy, it is possible to estimate a vessel's vector velocity based on two Doppler velocities measured along two different directions. In this article, a newly developed T/R-R composite compact HFSWR system is introduced, and a corresponding vessel velocity estimation method which employs a target's radial velocity and elliptical velocity, respectively, measured by the monostatic (T/R) and bistatic (T-R) settings is proposed. First, monostatic and bistatic tracks are independently generated using a multitarget tracking algorithm. Then, the obtained monostatic and bistatic tracks are matched using a track-to-track association method to determine the track pair belonging to each target. Subsequently, the associated track pairs are combined to produce fused tracks for improving positioning accuracy. Finally, vessel vector velocity is estimated based on the radial and elliptical velocities as well as the fused target position. Comparisons of vector velocity estimation results from radar field data with corresponding automatic identification system data demonstrate that the average root-mean-square-errors of the estimated speed and heading are 0.48 km/h and 3.9 $^{\circ }$ , respectively, which meets the practical requirements of a maritime surveillance system. Moreover, the velocity estimation error is analyzed via theoretical derivation and experimental verification. The proposed method shows good potential in further improving the tracking accuracy.

A robust constant false alarm rate detector based on the Bayesian estimator for the non-homogeneous Weibull Clutter in HFSWR

Target detection is a challenging problem in the complex detection environment of high-frequency surface wave radar (HFSWR). Due to the existence of various clutter and interferences, the detection environment in HFSWR is highly non-homogeneous with sharp/smooth clutter edges and multiple interfering targets. In this paper, we propose an effective detector for the non-homogeneous Weibull clutter in HFSWR with the use of prior information on the environmental context. Considering the spatial information of distribution parameters among neighboring cells and sparse information of interfering targets, the proposed method uses the Bayesian method to estimate distribution parameters and designs an objective function, which is composed of the data fidelity term, block matching 3-D frames and a sparsity regularization term. With the estimates of distribution parameters, the detection threshold is calculated accordingly and the presence of the target cell is decided. By minimizing the proposed model, the proposed method provides robust and accurate estimates of distribution parameters and can achieve the root-mean-squared error less than 0.12 in the simulated detection scenarios. Simulation and experimental results from a real HFSWR system are given to demonstrate the effectiveness of the proposed detector.

Vessel Tracking Using Bistatic Compact HFSWR

Bistatic and multi-static high-frequency surface wave radar (HFSWR) is becoming a prospective development trend for sea surface surveillance due to its potential in extending the coverage area, improving the detection accuracy, etc. In this paper, the vessel detection and tracking performance of a newly developed bistatic compact HFSWR system whose transmitting and receiving antennas are not co-located was investigated. Firstly, the representation of the target range and Doppler velocity concerning a bistatic HFSWR was derived and compared with that of a monostatic system. Next, taking the characteristics of target kinematic parameters into account, a target tracking method applicable to a bistatic HFSWR is proposed. The simultaneous target tracking results from both monostatic and bistatic HFSWR field data are presented and compared. The experimental results demonstrate the good performance in target tracking of the bistatic HFSWR and also show that an HFSWR system combining monostatic and bistatic modes has the potential to enhance the target track continuity and improve the detection accuracy.

A Higher-Order Singular Value Decomposition-Based Radio Frequency Interference Mitigation Method on High-Frequency Surface Wave Radar

Recently, high-frequency surface wave radar (HFSWR) has been widely applied in ocean surface dynamic parameter measurement. However, the radar echoes backscattered from ocean surface tend to be contaminated by the external radio frequency interference (RFI), and the mitigation of RFI becomes an intractable problem, especially for wide beam HFSWR. The HFSWR measured data are multichannel. The higher-order singular value decomposition (HOSVD) is an effective method to improve the accuracy of subspace estimation by exploiting this multidimensional structure. In this article, we develop an RFI mitigation method based on the HOSVD algorithm and orthogonal subspace projection. Simulations indicate that, compared with the previous orthogonal subspace projection RFI cancellation schemes, the proposed method has significant advantages in keeping the desired signals while suppressing the interference. The proposed method is applied to the experimental data of the HFSWR with severe RFI. The ocean surface currents inverted from the RFI-mitigated data agree reasonably with the tidal features in the radar detection area. After RFI mitigation, the performance of the HFSWR system is significantly improved on the effective current detection range and precision.

Stationary Time Statistical Property of Ionospheric Clutter in High-Frequency Surface-Wave Radar

In HFSWR (high-frequency surface-wave radar) system, the detection performance is impacted seriously by ionospheric clutter. Frequency selection is an effective method to avoid the effect of ionospheric clutter. The key to the method is the stationarity of ionospheric clutter over a period of time. This paper mainly researches the stationary time statistical property of the ionospheric clutter. A large number of real data including ionospheric clutter in HFSWR are processed and analyzed. It shows that ionospheric clutter in HFSWR has the characteristics of approximate stationarity within a period of time.

Modified Multi-Mode Target Tracker for High-Frequency Surface Wave Radar

In high-frequency surface wave radar (HFSWR), part of the radiation signal inevitably propagates upward and illuminates the target through the ionosphere due to the poor controllability of the antenna’s vertical pattern. As a result, a target may have several echoes from different propagation modes, which affect target detection and tracking and is usually unmanageable in existing HFSWR systems. Without the information of the elevation angle, it is difficult to distinguish the propagation mode of measurements during the target detection phase. This paper makes the first attempt to propose a multi-mode target tracker for HFSWR to solve this problem during the target tracking phase. The multipath probability data association (MPDA) tracker is capable of exploiting multipath target signatures of discrete propagation modes and has been widely used. Based on this, we construct a modified multi-mode probability data association tracker for HFSWR to suppress false tracks caused by multiple propagation modes. Numerical simulations demonstrate that this novel tracker can effectively and accurately track the target from the measurements under multiple propagation modes in HFSWR. The processing results of the actual data collected in Weihai, China indicate that this tracker is of great significance for practical applications.

Significant wave height extraction using a low‐frequency HFSWR system under low sea state

High-frequency surface wave radar (HFSWR) systems are usually located on the coast and measure electromagnetic backscatter from the sea in the 3-30 MHz band. Based on the theory of ground wave propagation, the detection distance of a low-frequency HFSWR system could be longer that of a high-frequency one. Therefore, a low-frequency HFSWR system is more suitable to extract the significant wave height at a distance. The Barrick's algorithm-based significant wave height extraction method is widely used, and the accuracy of the method has been validated by a large number of experiments. However, it is important to note that under the low sea state, the wave conditions of the data recorded by a low-frequency HFSWR system may not satisfy the wave height condition required in Barrick's algorithm. Hence, the algorithm may produce unreliable results. To address this issue, a method based on Barrick's algorithm, a new non-linear model, and the radial basis function neural network is proposed. Simulations on real-world data collected on the coast of the Tianjin from 1 May 2015 to 25 May 2015 support the method developed.

Experimental analysis of a HF hybrid sky-surface wave radar

High-frequency (HF) radar systems exploit refractive and diffractive properties of HF waves to sense targets beyond the line of sight. HF skywave radars use the ionosphere medium to reflect the signals and thus illuminate targets of thousands of kilometers away, but they suffer from the close-distance blind zone and severe ionosphere decorrelation effect. HF surface wave radars exploit the diffraction of vertically polarized waves to detect targets within hundreds of kilometers offshore. But the diffracted surface wave is highly attenuated, and the radar has the vertical incidence ionospheric clutter problems. High-frequency hybrid sky-surface wave radar (HFSSWR) is a novel radar configuration containing a skywave transmitting path and a surface wave receiving path, which is a natural combination of HF skywave and HF surface wave radar systems [1]. In addition, multiple separate receiving sites can compose a multistatic network and operate with only one transmitter. The system configuration is flexible.

Knowledge-Based Generalized Side-Lobe Canceller for Ionospheric Clutter Suppression in HFSWR

High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC) has been proved to be an effective algorithm for clutter suppression in theory, but it suffers from the performance degradation for some non-ideal conditions in practice. The most intolerable shortcoming is the signal to noise ratio (SNR) loss caused by the residual signal in the secondary data. In this paper, a knowledge-based GSC (KB-GSC) method has been proposed via an adaptive single notch filter design to reject the residual signal for reducing the SNR loss. The feasibility and availability has been demonstrated by measured data.

MIMO High Frequency Surface Wave Radar Using Sparse Frequency FMCW Signals

The heavily congested radio frequency environment severely limits the signal bandwidth of the high frequency surface wave radar (HFSWR). Based on the concept of multiple-input multiple-output (MIMO) radar, we propose a MIMO sparse frequency HFSWR system to synthesize an equivalent large bandwidth waveform in the congested HF band. The utilized spectrum of the proposed system is discontinuous and irregularly distributed between different transmitting sensors. We investigate the sparse frequency modulated continuous wave (FMCW) signal and the corresponding deramping based receiver and signal processor specially. A general processing framework is presented for the proposed system. The crucial step is the range-azimuth processing and the sparsity of the carrier frequency causes the two-dimensional periodogram to fail when applied here. Therefore, we introduce the iterative adaptive approach (IAA) in the range-azimuth imaging. Based on the initial 1D IAA algorithm, we propose a modified 2D IAA which particularly fits the deramping processing based range-azimuth model. The proposed processing framework for MIMO sparse frequency FMCW HFSWR with the modified 2D IAA applied is shown to have a high resolution and be able to provide an accurate and clear range-azimuth image which benefits the following detection process.