The coast–ship bistatic high-frequency surface wave radar (HFSWR) not only has the anti-interference advantages of the coast-based bistatic HFSWR, but also has the advantages of maneuverability and an extended detection area of the shipborne HFSWR. In this paper, theoretical formulas were derived for the coast–ship bistatic radar, including the first-order sea clutter scattering cross-section and the Doppler frequency shift of moving targets. Then, simulation results of the first-order sea clutter spectrum under different operating conditions were given, and the range of broadening of the first-order sea clutter spectrum and its influence on target detection were investigated. The simulation results show the broadening ranges of the right sea clutter spectrum and left sea clutter spectrum were symmetric when the shipborne platform was anchored, whereas they were asymmetric when the shipborne platform was underway. This asymmetry is primarily a function of platform velocity and radar frequency. Based on experimental data of the coast–ship bistatic HFSWR conducted in 2019, the broadening range of the sea clutter and the target frequency shift were analyzed and compared with simulation results based on the same parameter configuration. The agreement of the measured results with the simulation results verifies the theoretical formulas.

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.

Ionospheric clutter is a major factor affecting the performance of high frequency surface wave radar (HFSWR). Previous studies have been mainly focused on the development of ionospheric clutter suppression methods involving delicate signal processing techniques or additional antennas. However, ionospheric clutter originates from the interaction between HF waves and the ionosphere, thus it contains the characteristics of the latter. Therefore, ionospheric clutter can be analyzed to obtain the parameters of the reflecting ionosphere, expanding the value of HFSWR. This paper presents the preliminary coordinated observation and analysis results of the characteristics of the ionosphere at mid-latitudes of China using both HFSWR and ionosonde. The results demonstrate the existence of an oblique skywave propagation path (0.5 jump, 1 jump, etc.) in addition to the vertical reflection path and ionosphere-ocean mixed path. HFSWR beams were also found split into an O-trace and an X-trace after entering the ionosphere. Furthermore, range-folded ionospheric echoes are related to the short-term thickening of the F2-layer, showing strong fluctuation in the range-Doppler (RD) spectrum of HFSWR. These observed characteristics of ionospheric echoes are useful for the development of an efficient ionospheric clutter suppression algorithm for HFSWR and further investigation of the ionospheric mechanisms at mid-latitudes.

This paper presents a novel method for clutter suppression and target detection on the Range-Doppler (RD) spectrum of High Frequency Surface Wave Radar (HFSWR). The approach replaces the traditional classification layer of the residual neural network with a regression layer, forming the Residual Regression Network (RRN). Subsequently, a target-embedded dataset is constructed using real measurement data and supplemented simulation data to train the network in learning the characteristics of targets, clutter, and noise interference in the RD spectrum of HFSWR, thus generating a spectrum containing only target information. Introducing an exponential weight control factor, the network output is exponentially weighted and Hadamard multiplied with the original input’s spectrum to obtain the final spectrum for detection. This final spectrum is then fed into the Constant False Alarm Rate (CFAR) detector for further analysis. Through the verification of multiple data sets of target embedded data and measured data and multiple experimental groups, this method demonstrates superior clutter suppression and target detection performance compared to existing methods. Operators can adjust the suppression level by modifying the exponential weight control factor and optimize detection performance by tuning the control factor and CFAR detector parameters according to specific operational requirements.

High-Frequency Surface Wave Radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this paper designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional Constant False Alarm Rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.

The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable approach to research typhoons. High-frequency surface wave radar (HFSWR) as an over-the-horizon radar can achieve real-time monitoring of an extensive sea area and space. This paper derived the gravity wave perturbation spectrum by handling high-frequency surface wave radar data during typhoons. The gravity wave spectrum data were examined by applying the chaos examination approaches of the Lyapunov exponent and phase-space reconstruction to the gravity wave spectrum data after processing and extraction. The reconstructed phase space had a specific shape in a certain direction, with the maximum Lyapunov exponent greater than zero. The gravity wave spectrum data are suggested to have chaotic properties through two chaos examination approaches. This paper demonstrated that the gravity waves observed by a radar have chaotic properties through the measurement data of HFSWR. While the chaotic properties suggest that observed gravity wave data are predictable in the short term, they are unpredictable in the long term. Predicting gravity wave data is important for understanding the chaotic properties of the atmosphere and for future gravity wave prediction.