High Frequency Surface Wave Radar Research Articles

Deghosting method for cross localization in distributed high frequency surface wave radar

AbstractIn this article, the challenging problem of multi‐target cross localization within a novel distributed high‐frequency surface wave radar (HFSWR) is tackled. This system consists of two transmitter‐receiver pairs, each of which can correspondingly generate a set of Range‐Doppler measurements. By associating the Range‐Doppler measurements from the same target, the individual target location can be derived using the associated measurements. However, when multiple targets are involved, it is unknown which measurements originate from the same target, leading to ambiguities in the measurement‐to‐measurement association. False associations can result in ghosts, ultimately degrading the overall system performance. To deal with the ghosts, a three‐stage deghosting method is proposed that incorporates the geometric characteristics of the system, the velocity features of the targets, and the motion features of the targets across multiple frames. The simulation results demonstrate the effectiveness of the proposed method compared with the other investigated deghosting algorithms. The proposed approach exhibits robust target detection capability while significantly reducing both the OSPA error and the cardinality error.

An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

Typhoon Early Warning and Monitoring Based on the Comprehensive Characteristics of Oceanic and Ionospheric Echoes from HFSWR: The Case of Typhoon Muifa

As devastating natural disasters, typhoons pose a tremendous threat to human society, making effective typhoon early warning and monitoring crucial. To address this challenge, High Frequency Surface Wave Radar (HFSWR), which can observe oceanic parameters such as typhoon wind fields in real time and even capture the dynamic changes in the ionosphere, has become an effective tool for typhoon monitoring. This paper investigates the interaction mechanisms about Typhoon-Acoustic Gravity Waves (AGWs)-Ionosphere, as well as Typhoon-Ocean Waves for HFSWR, and simulates these interaction processes within HFSWR. Then a typhoon early warning and monitoring scheme for HFSWR has been proposed: In the first stage, the S-shaped ionospheric disturbances observed by HFSWR are utilized as precursor signals for early typhoon warnings. In addition, the second stage involves analyzing changes in first-order oceanic echo spectral peak ratio to pinpoint when the typhoon eye enters the radar detection range, thus initiating the typhoon monitoring phase. Subsequently, the measured data from HFSWR collected during Typhoon “Muifa” (2212) in conjunction with the proposed scheme are evaluated in detail. The results indicate that AGWs generated by typhoons can propagate into non-typhoon areas within the detection range, causing S- shaped ionospheric disturbances and providing approximately 6 h of early warning. At around 8:05 (UTC+8), an increasing trend in the first-order spectral peak ratio was noted, indicating the entry of the typhoon eye into the detection range, which closely aligns with the official typhoon path and marks the transition to the monitoring phase. The proposed scheme is expected to enhance the capability for typhoon early warning and real-time monitoring in specific sea areas and mitigate the risks associated with typhoon-related disasters.

Sliding window SA-CNN-based CFAR detector for extended target in shipborne HFSWR

ABSTRACT In shipborne high-frequency surface wave radar (HFSWR), the change of platform speed or heading will cause variations in the extension of the Doppler spectrum for vessel targets located in different directions, subsequently resulting in alterations to the signal-to-noise ratio (SNR). These alterations in vessel target echo present challenges for manual parameter adjustments in traditional constant false alarm rate (CFAR) methods for shipborne HFSWR, thereby hindering the maintenance of stable target detection capabilities. In this paper, a self-attention-convolutional neural network (SA-CNN)-based CFAR detector is proposed, which transforms the detection problem into signal structure classification. First, the extension characteristics of vessel target echoes resulting from changes in speed or heading of the shipborne platform are quantitatively analysed, thereby guiding the selection of an optimal sliding window and constructing input vectors for the neural network. Subsequently, the SA-CNN is designed to efficiently extract the structural features of the signal and accurately predict the probability of target presence. Finally, the Monte Carlo method is used to control the false alarm rate effectively. Simulation and real dataset verification demonstrate that the proposed method exhibits superior detection performance compared to traditional methods in shipborne HFSWR, especially for detecting extended targets.

Long-term tracking for high-frequency surface wave radar based on heading constraint filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.

Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method.

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

Combination of the biologically inspired coupled system and high‐frequency surface wave radar at signal level

AbstractVirtual aperture extension of small aperture array has attracted wide attention in high‐frequency surface wave radar (HFSWR). A biologically inspired coupled (BIC) system is employed to virtually extend the array aperture. However, the existing researches on BIC only consider the array signal processing model and do not combine it with actual radar signal principle. To indeed apply the BIC system to HFSWR, two detailed methods which combine the BIC and HFSWR at signal level are proposed. A three‐dimensional signal model of HFSWR considering array processing was established and the entire signal processing was derived. Then, two combination methods, namely fast‐time domain (FTD)‐BIC and slow‐time domain (STD)‐BIC are proposed. The former implements the BIC before fast‐time processing, while the latter implements the BIC before slow‐time processing. The authors demonstrate that they can virtually extend the array aperture without affecting the target detection. Meanwhile, their capabilities in multi‐target scenarios are analysed and satisfactory conclusions are obtained. By numerical simulations and experiments, the array aperture and range‐Doppler (RD) spectrum of the standard HFSWR and BIC‐HFSWR are compared. The results show that while the performance of their RD spectrum is almost the same, BIC‐HFSWR has an enlarged virtual aperture than standard HFSWR.

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

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.

Submesoscale Short-Lived Eddies in the Southwestern Taiwan Strait Observed by High-Frequency Surface-Wave Radars

Surface currents obtained from the high-frequency surface-wave radars (HFSWRs) installed along the coast of Fujian Province are utilized to characterize submesoscale eddies in the southwestern Taiwan Strait from 29 January to 26 March 2013. The algorithm based on vector geometry (VG) has been applied to datasets and a total of 414 (161 anticyclonic and 253 cyclonic eddies) were obtained. Eddies with both rotations had a relatively short lifespan (≤3.7 h), and their radii were in the range of 5–22.5 km. Eddies with a lifespan of over 30 minutes were more likely to occur north of the Taiwan Strait shoals and move eastward or northeastward. The deviation of moving directions of eddies with a moving distance of more than 20 km was within 18°. Moreover, eddies could hardly hold their original forms with cyclones extending in the east-west and compressing in the north-south direction, and anticyclones were the opposite. The vorticity and strain rate were proportional to the square of the energy intensity (EI). This study shows that the array HFSWRs have a strong capability to observe short-lived submesoscale eddies.

Ship Formation Identification with Spatial Features and Deep Learning for HFSWR

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification.

Target Detection Method for High-Frequency Surface Wave Radar RD Spectrum Based on (VI)CFAR-CNN and Dual-Detection Maps Fusion Compensation

This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data.

Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa

Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions.

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.

Range Aliasing Effect of Frequency Modulation Interrupted Continuous Wave (FMICW) High-Frequency Surface Wave Radar – Ionospheric Clutter Examination

Range Aliasing Effect of Frequency Modulation Interrupted Continuous Wave (FMICW) High-Frequency Surface Wave Radar – Ionospheric Clutter Examination

Real-time Implementation of Joint Domain Localised Algorithm for High Frequency Surface Wave Radar using GPU

Real-time Implementation of Joint Domain Localised Algorithm for High Frequency Surface Wave Radar using GPU

Tropical Cyclone Eye Positioning Using Monostatic High-Frequency Surface Wave Radar

Tropical Cyclone Eye Positioning Using Monostatic High-Frequency Surface Wave Radar

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.