High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC) has been proved to be an effective algorithm for clutter suppression in theory, but it suffers from the performance degradation for some non-ideal conditions in practice. The most intolerable shortcoming is the signal to noise ratio (SNR) loss caused by the residual signal in the secondary data. In this paper, a knowledge-based GSC (KB-GSC) method has been proposed via an adaptive single notch filter design to reject the residual signal for reducing the SNR loss. The feasibility and availability has been demonstrated by measured data.

The linear sparse array (LSA) can enlarge the array aperture and significantly enhance the angle accuracy. However, if the array aperture of the LSA is comparable to the range resolution, a phenomenon known as range-angle coupling (RAC) will be generated. It will lead to biased estimates of range and angle. In this paper, we investigate the RAC in LSA under the far-field and narrow-band model. We employ the frequency modulated continuous wave (FMCW) signal and highlight how the RAC exists in our model. In particular, we analytically elaborate on the impact of the RAC on system performance in terms of resolution and accuracy. Two challenging problems are effectively resolved in this paper for parameter estimation. One is to separate the RAC from the range term to achieve the decoupling, and we propose a modified reduced-dimension multiple signal classification (RD-MUSIC) algorithm. The second critical problem is to identify the true angles from a great number of false peaks in the presence of the RAC. We propose an eigenvalue-based algorithm and construct a function whose peaks only point to the true angles, successfully eliminating the impact of false peaks. Simulation results demonstrate the effectiveness of the proposed algorithm.

Existing sparsity-driven blind-free range extension method for frequency-modulated interrupted continuous wave (FMICW) radar faces the issues of inaccurate reconstruction and sizeable computational burden. This paper proposes an accuracy-improved and efficient blind-free range extension method for FMICW radar via non-uniform sampling and density-guided sparse reconstruction to address the issues mentioned above. By improving the design of non-uniform sampling sequence, the spectral anti-aliasing performance can be improved. Then, density features are used to initialize the sparse regularization parameters of each scatterer. By utilizing different sparse regularization parameters within an observation scene, the proposed density-guided sparse reconstruction method is able to suppress the non-structured noise caused by nonuniform sampling while retaining the information of weak targets. Compared to existing sparsity-driven blind-free range extension method for FMICW radar, the proposed method improves the reconstruction accuracy and reduces the computational burden by reducing the number of iterations. Simulations and experiments on measured FMICW radar data demonstrate the effectiveness of the proposed method.

In order to adapt to the crowed High Frequency (HF) electromagnetic environment, improve the utilization rate of bandwidth and further realize the higher range resolution, the sparse random stepped frequency (SRSF) signal is introduced into HF radar. Signal model based on compressive sensing (CS) theory is put forward to reconstruct the range spectrum of targets. However, redundancy is generated due to external factors or unreasonable parameters in the process of estimating. Therefore, in this paper, key external factors affecting the range estimation performance are analyzed in detail. These factors mainly include mismatched distance redundancy, oversample redundancy, and also distortion from unknown velocities of moving targets. Specific elimination methods are also provided. Comparison between before and after elimination shows the effectiveness of the proposed method.

In marine remote sensing, the phenomena of Doppler ambiguity widely exist in the velocity measurements of fast-moving targets in high-frequency surface wave radar (HFSWR) with frequency-modulated interrupted continuous waveform. The maximum Doppler that can be unambiguously measured for targets in HFSWR is restricted by the chirp repetition frequency. In this article, an approach for the extension of unambiguous Doppler in HFSWR is proposed based on Poisson disk sampling and sparse reconstruction. The start instants of adjacent chirps are set to be Poisson disk sampled. Taking advantage of the spectral antialiasing performance of the proposed nonuniform sampling strategy, the maximum unambiguous Doppler of HFSWR can be enlarged without decreasing the maximum aliasing-free range. The range-Doppler spectrum can be correctly reconstructed by utilizing a sparse reconstruction algorithm. Compared with the existing methods for resolving the Doppler ambiguity of HFSWR, the proposed method is able to monitor multitarget scenarios without introducing false alarms. The effectiveness of the proposed method is demonstrated by simulations and experiments based on measured HFSWR data.