This letter presents a novel signal processing method for HF surface wave radar that offers a solution to suppress significant sea clutter interference. Conventional clutter rejection methods, when the target signal and first-order sea clutter are mixed in the Doppler spectrum, tend to eliminate both the target signal and clutter without distinction. To address this problem, the frequency-modulated difference between the target signal and clutter is considered. Target echoes can be characterized as a linear-frequency modulation signal in long coherent time, while first-order sea clutter can be regarded as a superposition of several single-frequency signals. Based on the hybrid use of singular value decomposition (SVD) and the fractional Fourier transform (FRFT), SVD-FRFT filtering is conducted to suppress the components of the first-order sea clutter without impairing the target signals. The effectiveness of this method is illustrated by simulation and experimental results. It is further demonstrated that SVD-FRFT filtering can improve the signal-to-clutter ratio (SCR) to up to 40 dB for different SCRs.

In this paper, a novel shore-based S-band microwave Doppler coherent wave radar (Microwave Ocean Remote SEnsor (MORSE)) is designed to improve wave measurements. Marine radars, which operate in the X band, have been widely used for ocean monitoring because of their low cost, small size and flexibility. However, because of the non-coherent measurements and strong absorption of X-band radio waves by rain, these radar systems suffer considerable performance loss in moist weather. Furthermore, frequent calibrations to modify the modulation transfer function are required. To overcome these shortcomings, MORSE, which operates in the S band, was developed by Wuhan University. Because of the coherent measurements of this sensor, it is able to measure the radial velocity of water particles via the Doppler effect. Then the relation between the velocity spectrum and wave height spectrum can be used to obtain the wave height spectra. Finally, wave parameters are estimated from the wave height spectra by the spectrum moment method. Comparisons between MORSE and Waverider MKIII are conducted in this study, and the results, including the non-directional wave height spectra, significant wave height and average wave period, are calculated and displayed. The correlation coefficient of the significant wave height is larger than 0.9, whereas that of the average wave period is approximately 0.4, demonstrating the effectiveness of MORSE for the continuous monitoring of ocean areas with high accuracy.

This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial-temporal joint estimation has been proposed. However, because of the lack of spatial-temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods.

Radio frequency interference (RFI) is a common source of interference for high-frequency surface wave radar (HFSWR) since the HF band is shared among many radio services. The existence of RFI greatly degrades the detection performance of HFSWR. On the basis of a detailed analysis of the characteristics of RFI, a new RFI mitigation method based on joint fractional Fourier transform (FRFT) and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, the optimum transform order of the FRFT is obtained through a two-level peak search. Subsequently, echoes that contain RFI are transformed into the fractional Fourier domain and are then decomposed into a number of intrinsic mode functions (IMFs) via CEMD. The RFI is mitigated by implementing detection and localization in each IMF. The reconstructed signal is then transformed into the time domain via an inverse FRFT, and the mitigated signal is finally obtained. Processing results using experimental data indicate that the proposed method can effectively suppress RFI without losing echoes.

Radio frequency interference (RFI) is a common interference source in high-frequency surface wave (HFSW) radar. Its existence degrades the performance of HFSW radar greatly and makes it necessary to find an effective method to mitigate the interferences. There are two kinds of RFI in the experimental data. One is transient RFI, which is usually suppressed by temporal processing. The other is nontransient RFI, which is suppressed by adaptive beamforming methods. However, the temporal processing techniques suffer performance loss in nontransient cases, whereas the adaptive beamforming methods need spatially structuring, which is difficult to meet within the coherent integration time (CIT) of a few minutes. The fact that the experimental data are usually interfered by two kinds of RFI over a CIT motivates us to find a unified method for interference mitigation. In this letter, a new method based on complex empirical mode decomposition (CEMD) is proposed. CEMD is a local decomposition algorithm that can decompose the echoes and the RFI including transient and nontransient RFI into different intrinsic mode functions (IMFs). Then, the IMFs that correspond to RFI are processed. Experimental results indicate that the proposed method can effectively mitigate both kinds of RFI, and improve the signal-to-noise ratio without losing echoes.

The characteristics of radio frequency interference (RFI) are analyzed for high-frequency surface wave radar (HFSWR), which adopts a linear frequency-modulated interrupted continuous wave. If RFI enters the receiver, it contaminates the positive and negative frequency range bins simultaneously, whereas the negative frequency range bins contain the RFI and noise only. Moreover, RFI is always spatially structured over a short duration. Based on the above characteristics, a new orthogonal projection filtering algorithm is proposed for RFI cancelation. The proposed method uses the information of antenna channel and sweep dimensions in negative frequency range bins to estimate the interference covariance matrix. To suppress RFI, the signals of interest in the positive range bins are projected onto the interference subspace, which is obtained by eigendecomposition of the covariance matrix. Experimental results suggest that the proposed method can achieve effective RFI cancelation by using data collected by a multifrequency HFSWR.