Ship detection at the sea surface is important for improving human marine activities. Most existing ship detection methods for high-frequency surface wave radar (HFSWR) are based on peak and constant false alarm rate (CFAR) detection and require a coherent integration time (CIT) of several minutes. However, in such a long period, the target may not be stationary. To account for the nonstationary property, a time-frequency analysis (TFA)-based ship detection and direction finding (DF) method is proposed for HFSWR. Target ridges on the TF representation (TFR) of the echo data are detected first. Next, array snapshots are formed by sampling the extracted ridges and are used to estimate the direction of arrival (DOA). The processing results of the radar data collected at Dongshan, Fujian Province, China, show that the proposed method outperforms the CFAR method with both increased detection rates and decreased DF errors, especially under relatively low signal-to-noise ratio (SNR) scenarios.

High-frequency (HF) radars have gained much attention in recent years due to their remarkable capabilities in remote sensing of sea surface states. Radio frequency interference (RFI) should be suppressed before extraction of useful information from the radar echoes. Element instead of array-based interference suppression method is preferred for a small-aperture radar, and it is still more difficult to deal with the nonstationary interference. In this letter, a short-time range domain cancellation method for RFI suppression is proposed, whose advantage over the global one is twofolded: better suppression performance and lower computation cost. Processing results of real data collected by OSMAR-S, a portable HF radar, show the validness of the short-time method for nonstationary RFI suppression. The method can greatly improve the anti-interference capability and detection performance of the HF radar.

Radio frequency interference (RFI) is often a major problem in high-frequency (HF) radar operation, and until now, few existing methods worked very well to suppress dense RFI. In this letter, a theoretical analysis of the range-domain correlation of RFI in HF chirp radar is presented, and the relation between the correlation functions of RFI in range and fast time domain is given; based on this, a new method for RFI suppression is developed. The echo signals at the reserved range bins are used to construct an interference subspace, and then, the signals at the range bins of interest are projected onto its orthogonal subspace to suppress the RFI. When applied to radar signal processing, the method suppressed dense nighttime RFI effectively. It greatly improves the radar's detection performance in dense RFI.

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 conventional second-order Bragg-spectrum-based wave height extraction method is often susceptible to external noise and spatial aliasing. To improve the wave height estimate, we turn to the first-order Bragg peaks and propose a new method which directly estimates the wave height from them. The key point is to confirm and use the unsaturated property of the first-order Bragg spectral power. The quantitative relation between the first-order Bragg peak power and the significant wave height can be established with the help of an in situ wave buoy, and consequently, the wave height is to be read out from the curve via the maximum Bragg peak power on one range cell. The first-order method is validated by a two-month-long data set collected by the OSMAR-S radar at 13 MHz. Compared with the second-order method, the improvement is obvious under low and moderate sea states. The new method opens the way for wider use of the first-order Bragg peaks in wave height extraction by high-frequency radars.