To solve the problem of nonhomogeneous clutter suppression for moving target detection in High Frequency Surface Wave Radar (HFSWR), a novel nonhomogeneous clutter detector (NHD) is present in this paper. This novel NHD makes an analysis for the clutter constituents with single snapshot based on the over-determined linear equations in space-time adaptive processing (STAP) and distinguish the nonhomogeneous secondary data from the whole secondary data set through calculating the correlation coefficients of the secondary data.

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.

A robust adaptive beamforming algorithm is proposed based on the precise interference-plus-noise covariance matrix reconstruction and steering vector estimation of the desired signal, even existing large gain-phase errors. Firstly, the model of array mismatches is proposed with the first-order Taylor series expansion. Then, an iterative method is designed to jointly estimate calibration coefficients and steering vectors of the desired signal and interferences. Next, the powers of interferences and noise are estimated by solving a quadratic optimization question with the derived closed-form solution. At last, the actual interference-plus-noise covariance matrix can be reconstructed as a weighted sum of the steering vectors and the corresponding powers. Simulation results demonstrate the effectiveness and advancement of the proposed method.

High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

In small-aperture high frequency surface wave radar, the main-lobe clutter all can be seen as a more severe space spread clutter under the influence of the smaller array aperture. It compromises the detection performance of moving vessels, especially when the target is submerged in the clutter. To tackle this issue, an improved spread clutter estimated canceller, combining spread clutter estimated canceller, adaptive selection strategy of the optimal training samples and rotating spatial beam method, is presented to suppress main-lobe clutter in both angle domain and range domain. According to the experimental results, the proposed algorithm is shown to have far superior clutter suppression performance based on the real data.

Observations of gravity waves based on High Frequency Surface Wave Radar can make contributions to a better understanding of the energy transfer process between the ocean and the ionosphere. In this paper, through processing the observed data of the ionospheric clutter from HFSWR during the period of the Typhoon Rumbia with short-time Fourier transform method, HFSWR was proven to have the capability of gravity wave detection.

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

In the user-congested high-frequency band, radio frequency interference (RFI) is a dominant factor that degrades the detection performance of high-frequency surface wave radar (HFSWR). Up to now, various RFI suppression algorithms have been proposed while they are usually inapplicable to the compact HFSWR because of the minimal array aperture. Therefore, this letter proposes a novel RFI mitigation scheme for compact HFSWR, even for single antenna. The scheme utilized the robust principal component analysis to separate RFI and target, based on the time-frequency distribution characteristics of the RFI. The effectiveness of this scheme is demonstrated by the measured data, which can effectively suppress RFI without losing target signal.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.

The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

To improve the direction-of-arrival estimation performance of the small-aperture array, we propose a source localization method inspired by the Ormia fly’s coupled ears and MUSIC-like algorithm. The Ormia can local its host cricket’s sound precisely despite the tremendous incompatibility between the spacing of its ear and the sound wavelength. In this paper, we first implement a biologically inspired coupled system based on the coupled model of the Ormia’s ears and solve its responses by the modal decomposition method. Then, we analyze the effect of the system on the received signals of the array. Research shows that the system amplifies the amplitude ratio and phase difference between the signals, equivalent to creating a virtual array with a larger aperture. Finally, we apply the MUSIC-like algorithm for DOA estimation to suppress the colored noise caused by the system. Numerical results demonstrate that the proposed method can improve the localization precision and resolution of the array.

High Frequency Surface Wave Radar holds significant potential in sea detection. However, the target signals are often surpassed by substantial sea clutter and ionospheric clutter, making it crucial to address clutter suppression and extract weak target signals amidst the strong noise background.This study proposes a novel method for separating weak harmonic target signals based on local tangent space, leveraging the chaotic feature of ionospheric clutter.The effectiveness of this approach is demonstrated through the analysis of measured data, thereby validating its practicality and potential for real-world applications.

The performance of target detection and tracking is primarily limited by ionospheric interference in High Frequency Surface Wave Radar (HFSWR). Joint Domain Localised (JDL) has been proved to be an effective algorithm for ionospheric clutter suppression in HFSWR. However, the implementation of JDL in the traditional CPU platform cannot afford the real-time requirement in HFSWR. With the help of the tremendous parallel computational horsepower in GPU, in this paper we investigate the real-time implementation of JDL algorithm for HFSWR using Graphics Processing Unit (GPU). We also perform a comparative analysis in terms of the performance using the CPU-based implementation and the GPU-based implementation. Experimental result shows that the GPU-based implementation accelerates the computation by over 24.72 times as compared to the CPU-based implementation which meets the real-time requirement of HFSWR.