High Frequency Surface Wave Radar Research Articles

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.

Man-Made Radio Frequency Interference Suppression for Compact HF Surface Wave Radar

High-frequency surface wave radar (HFSWR) suffers from a man-made interference because its amplitude is high enough to mask the Bragg scattering signal. Although several methods have been proposed for resolving this problem, they are inapplicable to compact HFSWR due to their antenna structures. This letter proposes an effective method of suppressing man-made radio frequency interference for compact HFSWR. The proposed method is composed of man-made interference detection and suppression by using regression based on probabilistic signal model. The proposed method is demonstrated in comparison with conventional methods in terms of root-mean-square error in experiments using synthetic and real data. The results show that the proposed method outperforms other methods in both simulated and practical situations.

Radio Frequency Interference Cancelation in High-Frequency Surface Wave Radar Using Orthogonal Projection Filtering

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.

First‐order ocean surface cross‐section for shipborne HFSWR incorporating a horizontal oscillation motion model

To investigate the characteristic of ocean surface backscatter echo, the first-order ocean surface cross-section for shipborne high-frequency surface wave radar (HFSWR) incorporating a horizontal oscillation motion model is mathematically derived. The horizontal oscillation motion includes yaw, sway and surge. Simulation results show that the horizontal oscillation motion can induce more additional peaks in Doppler spectrum due to combined oscillation motions, and the amplitudes as well as frequency locations of these motion-induced peaks are determined by the amplitude and frequency of the oscillation motion. Furthermore, the Bragg and motion-induced peaks are spread due to the forward movement of the ship. These spreading peaks overlap each other as the ship speed increases, which may severely influence moving target detection and ocean remote sensing. However, different characteristics of the first-order cross-sections for the true and ambiguous wind directions provide a new idea to measure ocean surface wind direction by shipborne HFSWR with a single receiving antenna.

Using alpha-phase modulation method to solve range ambiguity for high frequency surface wave radar

A new type of high frequency surface wave radar (HFSWR) system consisting of a close-range detection using surface wave path and a long-range detection using skywave path are proposed in this paper. An important portion of this system is to design a signal that can detect longer distance targets, which is the main content of this paper. To minimize the changes to the existing radar system, this paper phase-modulates the original transmitted signal to mitigate range ambiguity. This method is applicable to all kind of single pulse repetition frequency (PRF) original transmitted signal and can manage the received signal in the region of eclipse (where the received signal is incomplete). The simulation results of different original transmitted signals (constant-frequency pulse and P4 phase-coded signals) verify that the long-range target can be correctly detected through this method. Finally, the real data collected at Weihai, China is processed. Comparing the flight information of Automatic dependent surveillance-broadcast (ADS-B) at the time when the data was recorded, two tracks that match the actual flight information in course and positions were found near 900 km (Shuozhou), flying to Hohhot.

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.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar (HFSWR) and automatic identification system (AIS) are the two most important sensors used for vessel tracking. The HFSWR can be applied to tracking all vessels in a detection area, while the AIS is usually used to verify the information of cooperative vessels. Because of interference from sea clutter, employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks. Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency. A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS. Since different systematic biases exist between HFSWR frequency measurements and AIS measurements, AIS information is used to estimate and correct the HFSWR systematic biases at each frequency. First, AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm. From the association results of the cooperative vessels, the systematic biases in the dualfrequency HFSWR data are estimated and corrected. Then, based on the corrected dual-frequency HFSWR data, the vessels are tracked using a dual-frequency fusion joint probabilistic data association (JPDA)-unscented Kalman filter (UKF) algorithm. Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

Radio Frequency Interference Mitigation for High-Frequency Surface Wave Radar

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.

Measuring Ocean Surface Wind Field Using Shipborne High-Frequency Surface Wave Radar

Extraction of ocean surface wind field from data collected by shipborne high-frequency surface wave radar (HFSWR) is an ongoing challenge because of the inherent directional ambiguity and the effect of complicated platform motion on radar Doppler spectra. Here, a method for extracting the wind direction and speed from the spreading first-order radar Doppler spectra is first presented. First, the mathematical model of the wind direction versus a variable spreading parameter is developed. Moreover, based on the spreading characteristic of the first-order spectra, an approach for simultaneously determining the unambiguous wind direction and the unique spreading parameter with a single receiving antenna is presented. Furthermore, the relationship between the wind speed and the spreading parameter is derived on the basis of the relationship between the drag coefficient and the spreading parameter, and the wind speed can be determined. Therefore, the wind field of ocean area covered by shipborne HFSWR can be measured by sequentially exploiting the presented method, which is more beneficial for shipborne HFSWR because of smaller installation space and less cost. Simulation results and discussions of basic applications show the feasibility of wind field measurement in shipborne HFSWR. Experimental results validate the presented method and evaluate the detection accuracy and distance limit. The range for wind field measurement is up to 120 km, which is the range for which the signal-to-noise ratio typically exceeds about 11 dB in the relevant first-order portions of the backscatter spectra. Comparisons between the radar-measured and forecasting or buoy-measured results show good agreement.

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.

Maritime over the Horizon Sensor Integration: High Frequency Surface-Wave-Radar and Automatic Identification System Data Integration Algorithm.

To obtain the complete operational picture of the maritime situation in the Exclusive Economic Zone (EEZ) which lies over the horizon (OTH) requires the integration of data obtained from various sensors. These sensors include: high frequency surface-wave-radar (HFSWR), satellite automatic identification system (SAIS) and land automatic identification system (LAIS). The algorithm proposed in this paper utilizes radar tracks obtained from the network of HFSWRs, which are already processed by a multi-target tracking algorithm and associates SAIS and LAIS data to the corresponding radar tracks, thus forming an integrated data pair. During the integration process, all HFSWR targets in the vicinity of AIS data are evaluated and the one which has the highest matching factor is used for data association. On the other hand, if there is multiple AIS data in the vicinity of a single HFSWR track, the algorithm still makes only one data pair which consists of AIS and HFSWR data with the highest mutual matching factor. During the design and testing, special attention is given to the latency of AIS data, which could be very high in the EEZs of developing countries. The algorithm is designed, implemented and tested in a real working environment. The testing environment is located in the Gulf of Guinea and includes a network of HFSWRs consisting of two HFSWRs, several coastal sites with LAIS receivers and SAIS data provided by provider of SAIS data.

An Orthogonal Projection Algorithm to Suppress Interference in High-Frequency Surface Wave Radar

High-frequency surface wave radar (HFSWR) has been widely applied in sea-state monitoring, and its performance is known to suffer from various unwanted interferences and clutters. Radio frequency interference (RFI) from other radiating sources and ionospheric clutter dominate the various types of unwanted signals because the HF band is congested with many users and the ionosphere propagates interference from distant sources. In this paper, various orthogonal projection schemes are summarized, and three new schemes are proposed for interference cancellation. Simulations and field data recorded by experimental multi-frequency HFSWR from Wuhan University are used to evaluate the cancellation performances of these schemes with respect to both RFI and ionospheric clutter. The processing results may provide a guideline for identifying the appropriate orthogonal projection cancellation schemes in various HFSWR applications.

Spatial polarimetric time-frequency distribution based DOA estimation: combining ESPRIT with MUSIC

Spatial spectrum estimation technology has always been the focused research project in an array signal processing of High Frequency Surface Wave Radar (HFSWR). This paper takes the polarization sensitive array for the model and combines with time-frequency analysis method for DOA estimation of HFSWR echo signals, which can make full use of the spatial, time-frequency, and polarized domain information of signals. The time-frequency MUSIC and ESPRIT algorithms of integrated polarization information are studied, and the improved algorithm is proposed based on the advantages and disadvantages of both algorithms. The improved algorithm first estimates the azimuth angles of signals roughly with the polarized time-frequency ESPRIT algorithm and then determines a small scope centered on each azimuth. Within the scope, a more accurate DOA-estimated value is got by spectral peak search with MUSIC algorithm, so it can save computation time on the basis of accuracy of the DOA estimation, and their performance is analyzed by computer simulations.

Target monitoring using small-aperture compact high-frequency surface wave radar

High-frequency surface wave radar (HFSWR) can detect and continuously track vessels beyond the horizon in excess of 200 nautical miles from the array [1]–[3]. Moreover, multiple HFSWRs can be used, and their detection results can be fused to improve the detection performance of marine target [13]–[15]. Most current HFSWRs for ship surveillance are large phased-array systems. The antenna arrays of such systems are hundreds or even thousands of meters wide [4], [5], [15]. Some of those radar systems require large fixed seaside radar sites or large transmit power. For those reasons, large-array HFSWR has not been widely applied.

Conceptual study on bistatic shipborne high frequency surface wave radar

High frequency surface wave radar (HFSWR) operates in a high frequency (HF) band (3–30 MHz) within which the surface wave propagation mode allows beyond-the-horizon detection of targets, as well as remote sensing of ocean parameters. Specifically, the vertically polarized electromagnetic waves at the lower end of the HF band (3–12 MHz) experience relative low losses when propagating over ocean water that enable the HFSWR to capture the echo signals from the sea surface, ships, and low-flying aircrafts far beyond the horizon.

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.

HF surface wave radar for tsunami alerting: from system concept and simulations to integration into early warning systems

An important property of the high-frequency (HF) band (3-30 MHz) is the unique ability of HF signals to propagate over long distances and to illuminate the Earth's surface beyond the horizon. Historically, over-the-horizon (OTH) HF radar systems were divided into two main categories: (1) those using sky wave propagation in different ionospheric layers and reaching thousands of kilometers and (2) others using the surface (ground) wave mode when the signals couple well to the conducting ocean surface and hence potentially enabling propagation and observations hundreds of kilometers from shore (depending on the radar parameters and operating conditions). Excellent descriptions of both types of OTH systems, including their characteristics and functionality, appear in [1]. Originally, both types of OTH radar were intended to fulfill military tasks of air and maritime surveillance far beyond the horizon. However, other systems, generally categorized as HF ocean radars, have been developed for civil applications, with remote sensing of the sea surface as a primary goal. These applications include spatial mapping of current velocity, significant wave height, wave spectra, and surface winds. A large number of experimental and commercial HF ocean radar systems have been deployed w.orldwide for remote-sensing purposes [2], [3].

Detection of Vessel Targets in Sea Clutter Using <italic>In Situ</italic> Sea State Measurements With HFSWR

The detection of vessel targets could be effectively resolved in a high-frequency surface wave radar (HFSWR). However, signals reflected from vessels are concealed by sea clutter in the Doppler spectrum, where such detections are performed. Consequently, differences between these features in the Doppler domain cannot be readily observed, which greatly increases the difficulty in detecting vessel targets. In this letter, in situ sea state information is utilized to facilitate the detection of targets within sea clutter. First, the sea clutter spectrum, which is absent of vessel, is constructed. Second, sensitive sea clutter features that are influenced by vessel targets are selected and analyzed. Third, anomalies in sensitive sea clutter features are detected by obtaining respective thresholds. Finally, vessel targets are identified by the synthesized anomaly detection. Experimental results demonstrate the effectiveness of the proposed method, and the vessels detected using the HFSWR are further verified using synchronous automatic identification system information.

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.

Theoretical Performance of Space-Time Adaptive Processing for Ship Detection by High-Frequency Surface Wave Radars

In this paper, we analyze the levels of performance of the (full or reduced versions of) space-time adaptive processing (STAP) algorithms applied to high-frequency surface wave radars (HFSWRs) for marine target detection to verify whether this family outperforms the usual moving target indication (MTI) algorithms. In fact, STAP algorithms have well-known drawbacks (e.g., large amount of secondary data). The propagation and backscattering of radar signals are included in the covariance matrix calculation. Since the propagation and backscattering have to be derived from physical features of the sea (e.g., sea Doppler) and the atmosphere, we also include the difference induced by the waveform (pulsed radar/chirp) in our study. Moreover, since the inverse covariance matrix is used in the target detection algorithms, matrix conditioning is also inspected for practical implementation reasons. We numerically detail the performance (signal noise clutter ratio and conditioning number of the STAP algorithms) with regards to the MTI performance for several configurations of radar and sea state. These results show that the improvements of signal noise clutter ratio are counterbalanced by a raise of the conditioning number, except in the case of eigencanceler approach. The sea-state parameters have few effects on the performance unlike to radar parameters.