In addition to sea state monitoring, high-frequency surface wave radar (HFSWR) has attracted more and more attention in ship detection and tracking. Constant false alarm rate (CFAR) detectors have been widely used to handle the complex properties of sea clutters. Due to the relatively high detection threshold, the performance of CFAR in detecting weak and nonstationary targets is often not good. To address this problem, time–frequency analysis (TFA) is involved in the ship detection and direction finding (DF) processing. First, the probability distribution model of sea clutter is achieved in the time–frequency (TF) domain. Corresponding to this sea clutter model, the decision thresholds of targets on the TF plane under different false alarm rates $P_{\mathrm {fa}}$ are calculated. Next, the array snapshots are formed by the spectral samples along each extracted TF ridge and later used in the DF process to give the direction of arrival (DOA). Experimental results show that, with the automatic identification system (AIS) records as the ground truth, the number of matched targets detected by the proposed TF-CFAR method is 5%–8% greater than that by the cell averaging (CA)-CFAR method. Moreover, the TF multiple-signal classification (MUSIC) also outperforms MUSIC with an improvement of 3.52° in the root-mean-square error (RMSE) of the DOA estimates under a low signal-to-noise ratio (SNR). In conclusion, the involvement of TFA can greatly improve the detection and DF performances of compact HF radar, particularly under the situations of low SNR and target nonstationarity.

The shipborne high-frequency ground wave radar (HFGWR) provides a better condition for sea surface target detection than the shore-based one because of its maneuverability and flexibility. However, the first-order Bragg spectra of the shipborne HFGWR would be broadened by the Doppler shift of the ship motion, which makes the vessel echo easily submerged. Even though some methods have been applied to suppress the broadened Bragg spectra for target detection, most of them are hard to achieve good result in a small-aperture radar. An orthogonal projection method to suppress the broadened Bragg spectra using a cross-loop/monopole array is proposed in this letter. The proposed method mainly includes two steps: 1) estimate the space of sea clutter in a spatial domain according to the ocean echo characteristics and 2) design an adaptive filter of single or multiple Doppler bins on the estimated clutter space to suppress the first-order spectra. After suppression, we use the multiple signal classification algorithm to estimate the incident direction of targets. The experimental data validate that the sea clutter is well suppressed and the target signal-to-clutter ratio is increased by the algorithm.

The direction-finding high-frequency (HF) radar that employs a crossed-loop/monopole antenna as receiving has been widely applied in sea surface dynamics remote sensing due to its compact footprint and remarkable performance. Nevertheless, the direction-of-arrival (DOA) estimation of this system remains challenging in the case of coherent signals, which imposes a great threat to the radar measurement accuracy but has rarely been concerned in the past. In this article, a novel method combining the covariance matrix reconstruction process and subspace estimation technology is proposed to deal with this problem. By exploiting the purely real property of the array manifold, the rank loss of the covariance matrix due to the coherence of signals is mitigated. Both the theoretical derivation and numerical analysis prove that the proposed method is valid in estimating two coherent signals, and the DOA estimation accuracy is mainly related to the signal-to-noise ratio (SNR) and angle separation of the signals. Finally, field experiment data are also used to validate the effectiveness of the proposed method.

Attributed to the maneuverability, the shipborne high-frequency surface wave radar (HFSWR) enables a larger coverage in ocean wind measurement than a shored-based system does. However, the antenna aperture of shipborne radar is usually limited by the ship size, which troubles the direction of arrival (DOA) estimation accuracy. In addition, the wind direction ambiguity caused by monostatic radar illumination remains challenging. In this article, an unambiguous wind direction estimation method based on a compact shipborne HFSWR is proposed. An equivalent dual-station model combining the successive radar data collected at two close locations is developed to solve the DOA ambiguity in wind direction estimation. A direction-finding algorithm incorporating the Doppler shifts due to ship motion and current shear is adopted to estimate wind direction from the ratio of the positive and negative Bragg peak powers. Moreover, three different wave directional spreading models are applied for performance comparison. Numerical simulation is conducted to evaluate the effect of dual-station configuration on the estimation accuracy. Finally, field experimental results are given to verify the correctness of the method. Radar-derived wind direction field agrees well with that provided by a numerical weather model.

Compact high-frequency (HF) radar is a promising tool for maritime surveillance owing to its over-the-horizon and all-weather ability. However, it typically suffers from low tracking accuracy due to limited azimuth resolution. Cross localization using multiple radars provides a solution, but track-to-track association among radars remains a critical challenge. Previous association methods are mainly subject to that each radar’s range and azimuth measurement errors are comparable and sufficiently small, which is not satisfied in HF radar. In this article, we propose a novel track association algorithm based on the target region match (TRM) strategy, which employs the joint range and azimuth gates intersection criterion to combine the effect of range and azimuth measurement errors. Considering the azimuth error is much larger than that of the range, their contribution to the track association is balanced through the area intersection operation. Both simulation and experiment results show that the proposed TRM method produces better association performance than the other investigated methods.