Shipborne high-frequency surface wave radar (HFSWR) has wide-area coverage capability and maneuverability compared to shore-based HFSWR systems. Due to the influence of platform motion, especially yaw motion, the traditional tracking before detection (TBD) method based on channel range-Doppler-time (RDT) becomes ineffective, resulting in track fragmentation or loss. In this letter, the specific effects of platform yaw motion on target detection are analyzed, and then, a TBD method based on the adaptive constructed beam RDT is proposed. The proposed method includes beam RDT dynamic adjustment and RDT data repair, which effectively addresses the problems caused by the yaw motion of the shipborne platform, such as the change of the target beam and the target moving out of the radar detection coverage area. Both cases lead to a reduction in target amplitude and signal-to-noise ratio (SNR), exacerbating the target track fragmentation or loss problem. Finally, experimental results using measured data demonstrate that the proposed method substantially enhances the target detection and tracking performance of shipborne HFSWR, extending the target tracking time and minimizing target track fragmentation.

Accurately identifying the tropical cyclone’s (TC) eye position and tracking its movement path is crucial for TC monitoring and warning. High-frequency surface wave radar (HFSWR) offers wide-range, all-weather, real-time marine observation. The wind field inversion results from HFSWR can be used to identify the eye position. However, due to the complex and rapidly evolving wind fields of storms, monostatic HFSWR encounters issues with wind direction ambiguity, making it difficult to accurately identify the eye position. This letter explored the wind direction inversion of HFSWR under TC conditions, correcting the wind direction inversion results by incorporating the central angle with the eye position as the center of the circle. Based on this, a method integrating wind direction inversion and eye position estimation was proposed. This method identified the fit eye position by searching for the wind direction results that best match the TC model. The measured HFSWR data during Typhoon Muifa in 2022 was obtained, and the eye position was estimated using the method in this letter. The results were compared with the typhoon track results given by the China Meteorological Administration National Satellite Meteorological Center (NSMC). The comparison indicates that the average discrepancy in observed eye positions is 13.58 km. This method demonstrates a reliable eye position estimation capability, which can support TC track monitoring and forecasting applications.

Due to the high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar (HFSWR), plot-to-track association methods using kinematic parameters alone may not achieve satisfactory performance. In this letter, the course consistency of vessels is analyzed, and a plot-to-track association method using statistical course modeling is proposed. First, vessel course data sequences are obtained by applying a course estimation method to each track, and each course data sequence is modeled using the Incremental Gaussian Mixture Model (IGMM) by considering the measurement uncertainty. Next, the measurements within the association gate of each track are connected with the last plot respectively to determine instantaneous courses, and the membership probability set that the instantaneous courses belong to the established IGMM is obtained using Bayes’ rule. Subsequently, the obtained membership probability values and kinematic parameters of each measurement are incorporated to calculate the association cost, and a cost matrix is obtained for tracks with shared candidate plots in the overlapped region of their association gates. Finally, the Hungarian algorithm is applied to the cost matrix to obtain plot-to-track association results. Plot-to-track association experiments using both simulated and field data were conducted, and experiment results demonstrate that the optimal sub-pattern assignment distance in latitudes and longitudes obtained by the proposed method is 0.001° lower than that of the Nearest Neighbor Data Association method on average and achieves a competitive performance over the Joint Probability Data Association method but with running time being reduced by 0.48 seconds for each frame.

Compared to shore-based high-frequency surface wave radar (HFSWR), shipborne HFSWR can overcome the constraints of a fixed radar site and extend its detection range. However, the radar echo is influenced by the movement of the shipborne platform, which in turn affects the target monitoring performance of the shipborne HFSWR. In this article, the radar echo model for shipborne radar is introduced, and the Doppler frequency shifts for different signals are given. Then, the characteristics of vessel target echoes for shipborne HF radar under various motion conditions are analyzed. Subsequently, the characteristics of spread sea clutter and its impact on target monitoring under different motion conditions are investigated. Moreover, land clutter, which is often neglected for shore-based HFSWR, is also investigated. Considering the combined effect of clutter blind zones caused by sea clutter and land clutter, the target monitoring capability of shipborne HFSWR under different motion conditions is evaluated, and then, a target monitoring scheme is proposed. In the target monitoring scheme, different navigation scenarios are used to adjust the platform motion state depending on different detection targets. Low-speed navigation scenario is appropriate for the monitoring of moving targets, whereas high-speed navigation scenario is suitable for detecting stationary targets or vessel target initially submerged in nonspread sea clutter. Finally, the clutter extraction results from measured data under different motion conditions and their impact on target monitoring are analyzed, and target monitoring results are provided and validated using field data.

Coast-transmit ship-receive (CTSR) bistatic high-frequency surface wave radar (HFSWR) can fully exploit the flexibility of moving platforms and the anti-interference advantages of bistatic radar. However, the platform motion can cause the spread of target echo in the frequency spectrum and reduction of amplitude, which is not conducive to detection. In this letter, a motion compensation method using a direct wave signal is proposed for CTSR bistatic HFSWR. First, the frequency shift characteristics of direct wave signal and target echo are analyzed by simulation. The simulation results show that the platform motion has a similar modulation form on both target echo and direct wave signal, and their difference is mainly caused by different azimuths. In addition, the direct wave signal is only affected by the platform motion, so it can be used to obtain the platform motion effect. Then, the proposed method uses a direct wave signal as the reference information to estimate and eliminate the phase modulation caused by the platform motion. At last, the effectiveness of the method is verified by the experiment based on simulated and measured attitude data.

Due to its low transmit power and reduced aperture size of a receiving antenna array, compact high-frequency surface wave radar (HFSWR) suffers from low detection probability, low positioning accuracy, and high false alarm rate. In a multi-target tracking scenario, similar kinematic parameters of adjacent targets raise challenges to the track-to-track association procedure. Taking the measurement uncertainty of compact HFSWR into consideration, a track-to-track association method based on maximum likelihood estimation (MLE) for T/R-R composite compact HFSWR is proposed. Firstly, a multi-target tracking algorithm is applied to plot data sequences acquired by both T/R monostatic and T-R bistatic radars to produce two track sets. Then, the measurement errors of range, azimuth, and Doppler velocity are calculated using the obtained radar track and corresponding AIS track data, and a Gaussian distribution model is derived through probability distribution fitting. Subsequently, likelihood functions are established using the obtained Gaussian distribution model to calculate the association cost of tracks respectively for T/R monostatic and T-R bistatic radars, and a cost matrix is obtained. Finally, the Jonker-Volgenant-Castanon (JVC) assignment algorithm is applied to the cost matrix to determine associated track-track pairs. Track-to-track association experiments using both simulated and field data were conducted, and the association performance of the proposed method is compared with that of Mahalanobis distance-based nearest neighbor (NN) method. Experimental results demonstrate that the proposed method can effectively resolve association ambiguity and achieve correct track-to-track association in track crossing and adjacent multi-target scenarios.

High-frequency surface wave radar (HFSWR) plays an important role in vessel target surveillance. However, HFSWR’s inaccuracy of azimuth estimation caused by wide beams severely limits its detection ability. To solve this problem, a novel direction of arrival (DOA) estimation method based on extreme learning machine optimized by particle swarm optimization (PSO-ELM) is proposed to improve azimuth estimation accuracy for HFSWR. This method can obtain the optimal solution without searching the whole angle range of HFSWR. Specifically, PSO optimizes the input weight and hidden layer bias of ELM to obtain optimal parameters for improving the estimation performance. Based on the optimized parameters, the ELM network can give an optimal azimuth estimation in the sense of least squares and minimal norm. The sample sets used for PSO-ELM training are obtained by matching the points detected by HFSWR with the target points reported by an automatic identification system (AIS) on the range–Doppler (RD) spectra. The performance of DOA estimation is verified by field HFSWR data. The experimental results show that the new method has lower root-mean-square error and higher computational efficiency in comparison to the typical DOA estimation methods, such as digital beam forming (DBF) and multiple signal classification (MUSIC). It also uses the machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR).

For onshore high-frequency surface-wave radar (HFSWR), target parameter estimation focuses mainly on distance and velocity demodulation, then on azimuth determination. However, these parameters are difficult to solve accurately for shipborne HFSWR targets due to additional modulation on the echo signal introduced by the forward and six-degree-of-freedom (6-DOF) movement of the platform, causing target points spread and shift in radar Doppler spectra. To overcome this difficulty, the influence of platform motion on target detection is mathematically analyzed in terms of echo signal processing, and then theoretical equations are derived to correct the bias in measurements of the target's state and features. Furthermore, to meet the requirement of shipborne HFSWR installed in limited space, the direction of arrival (DOA) estimation of irregular radar arrays with unequal intervals and arbitrary numbers of antennas is also analyzed. With the derived formulas, the parameters of the shipborne HFSWR target, including the range, radial velocity, and azimuth, can be accurately estimated with the help of inertial navigation system (INS) data. Moreover, both the simulation and the field experiment results validate the theoretical analysis and the derived equations.

Compared with transmit/receive (T/R) monostatic High-frequency surface wave radar (HFSWR), the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment and large coverage because of the separation between the radar transmitter and receiver locations. In this paper, a target monitoring method with bistatic compact HFSWR was proposed. The results of a target detection experiment using T-R bistatic compact HFSWR conducted in 2015 were presented, and the validity of the method and the tracing results were verified by using synchronous automatic identification system (AIS) data.

Small-aperture compact high-frequency surface wave radar (HFSWR) suffers from low azimuth accuracy for target detection due to its wide beamwidth. Multitarget tracking (MTT) algorithms, when applied to the raw target detection data of HFSWR, fail to effectively filter the target azimuths, and thus, resulting in inaccurate target tracks and courses. In this article, a vessel azimuth and course joint re-estimation method by exploring Doppler velocity and the information accumulated from consecutive observations is presented. It begins with applying an MTT algorithm to a measured target states data sequence acquired by HFSWR to establish initial target tracks, from which the measured range, azimuth, and radial velocity data sequences are obtained. Then, the azimuth trend is extracted from the obtained azimuth data sequence as roughly corrected azimuth estimates, with which the target locations are roughly corrected. Subsequently, target speeds and initial courses are estimated based on the roughly corrected location data sequence, followed by a data selection procedure based on proposed control parameter rules to select the qualified data for calculating the projected angles in terms of speed and direction, separately. Eventually, the target azimuth data sequence is further refined using a linear azimuth error model, whose parameters are obtained by minimizing the difference between the projected angles using a constrained optimization method. Experimental results from field data demonstrate that the proposed method can estimate the target azimuths with significantly improved accuracy. The deviations of the corrected target locations are considerably reduced, and the accuracy of course estimation is enhanced.

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.