It is known that the data acquisition and processing system plays an important role in radar target detection system. In order to meet the requirements of real-time processing and accurate transmission of echo signals in high-frequency ground-wave radar (HFGWR) systems, a new acquisition and transmission framework utilizing the designed acquisition card based on the PCIe (peripheral component interconnect express) has been designed and is presented in this paper. The Xilinx FPGA (Field-Programmable Gate Array) chip Kintex7-XC7K325T is adopted as a hardware carrier in acquisition card. The hardware’s composition, analog front-end circuit, the DMA (Direct Memory Access) transmission, FPGA structure, ADC (Analog-to-Digital Converter) chip, and performance test of this card are showed and discussed. Currently, the acquisition card has been accomplished and applied in the practical system of HFGWR.

In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

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.

In a heterogeneous environment, the ionosphere is dynamically changing in the Earth’s middle latitude, and backscatter from fast-moving irregularities in the plasma can cause ionosphere clutter to extend. Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to these challenges, this research presents a multi-channel deep learning time–frequency feature filter framework (DL-TFF). Firstly, we observed the behavior of the ionosphere clutter for a long period of time before selecting the representative ionosphere clutter. Secondly, different transform techniques are applied to provide a time–frequency representation of the non-stationary echo signals, and representation results of different echo components are collected as a training set for feature learning. Thirdly, we design a multi-channel time–frequency feature learning network (MTF), which is responsible for mining discriminative time–frequency information between targets and different types of ionosphere clutter. Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets. Moreover, its suppression effectiveness is more ideal than the previous classical method.

Small‐array high‐frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on‐land resources. In small‐array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal‐to‐clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space‐time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small‐array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF‐JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF‐JDL is still applicable at lower SCRs of the target compared with JDL.