The South China Sea is a major shipping hub between the West Pacific and Indian Oceans. In this region, the demand for energy is enormous, both for residents’ daily lives and for economic development. Wave energy and wind energy are two major clean and low-cost ocean sources of renewable energy. The reasonable development and utilization of these energy sources can provide a stable energy supply for coastal cities and remote islands of China. Before wave energy and wind energy development, however, we must assess the potential of each of these sources. Based on high-resolution and high-accuracy wave field data and wind field data obtained by ERA-Interim reanalysis for the recent 38-year period from 1979–2016, the joint development potential of wave energy and wind energy was assessed in detail for offshore and nearshore areas in the South China Sea. Based on potential installed capacity, the results revealed three promising areas for the joint development of nearshore wave energy and wind energy, including the Taiwan Strait, Luzon Strait and the sea southeast of the Indo-China Peninsula. For these three dominant areas (key stations), the directionality of wave energy and wind energy propagation were good in various seasons; the dominant wave conditions and the dominant wind conditions were the same, which is advantageous for the joint development of wave and wind energy. Existing well-known wave energy converters (WECs) are not suitable for wave energy development in the areas of interest. Therefore, we must consider the distributions of wave conditions and develop more suitable WECs for these areas. The economic and environmental benefits of the joint development of wave and wind energy are high in these promising areas. The results described in this paper can provide references for the joint development of wave and wind energy in the South China Sea.

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 multitarget 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. First, a multitarget 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 automatic identification system (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 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-to-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 multitarget scenarios.

Due to its limited transmit power and smaller receiving antenna array, compact high-frequency surface wave radar often encounters increased challenges in detecting and tracking sea-surface targets continuously. In tracking scenarios with dense clutter or multiple targets, weak target signals are often missed due to improper detection thresholds, leading to track fragmentations during target tracking. In order to improve target detection probability and enhance target tracking continuity, a joint detection and tracking (JDT) paradigm, which establishes a closed loop between the detector and tracker, is proposed. When a target of interest is tracked, the tracker sends its predicted range, Doppler velocity, and azimuth back to the detector, then the detector builds a detection gate centered at the predicted range and Doppler velocity on the range–Doppler map. Within the detection gate, an optimal detection threshold dependent on the detection background and tracking environment is determined using reinforcement learning. In this way, a potential target plot may be detected with a higher detection probability and the detected plot is provided for track update. The proposed paradigm employs tracking information to provide adaptive detection parameters for specific targets through reinforcement learning to enhance both target detection and tracking performance. Experimental results with field data demonstrate that compared with traditional detection before tracking scheme, the proposed JDT paradigm achieves a superior performance with the average tracking time on target being increased by 13.33 min and the average missed detection rate being reduced by 0.8 $\%$ .

To address plot-to-track association ambiguities caused by low spatial resolution and measurement uncertainties of compact high-frequency surface wave radar (HFSWR) operating in complex maritime environments, a hybrid framework integrating a target state prediction method based on Transformer model and a plot-to-track association method using graph-based feature representation is proposed. In the target state prediction stage, the Transformer model employs self-attention mechanisms to capture long-term temporal dependencies in historical track data to improve the prediction accuracy of the plot-to-track association gate center. In the plot-to-track association stage, a graph autoencoder (GAE) is used to extract graph structure features of candidate measured plots from their corresponding range-Doppler (R-D) spectra. The GAE encodes the diffusion patterns and spatial correlations of radar echoes on the R-D spectrum into discriminative graph feature vectors. The graph feature vectors, which can enhance both inter-target discrimination and target-clutter differentiation, serve as the basis for association similarity computation. Experimental results demonstrate that the proposed framework significantly reduces target state prediction errors and suppresses false plot-to-track associations, achieving an average tracking time on target of 135.6 minutes, which is improved by 36% compared with the baseline method (motion model-based target state prediction combined with nearest neighbor data association using kinematic parameters).