Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

Detecting and tracking small drones is significant for security requirements, but is challenging since small drones typically have a low radar cross section (RCS), small size and hovering ability. Polarimetric analysis of returned radio wave is of great significance in detection and track of small drones. In this letter, the polarimetric analysis based on RCS and micro- Doppler signature of small drone rotor blade is firstly studied in VHF/UHF band. Both the simulated and measured RCS results confirm that the Horizontal-Horizontal (H-H) polarization is preferred for drone detection in low altitude angles at this frequency band. Moreover, the Method of Moments (MoM), a rigorous numerical method based on integral equation, is implemented to simulate the micro-Doppler signature generated by the rotating blade in VHF/UHF band. The results show that it is feasible to obtain the rotating speed from the micro-Doppler measurement, which is also verified by the experiment.

Crossed-loop/monopole antenna has been widely used in high frequency ground-wave radar for ocean surface remote sensing owing to its compact size. The multiple signal classification (MUSIC) algorithm is commonly adopted to achieve a favorable angular resolution for this broad beam antenna. However, the direction of arrival (DOA) estimation is always troubled by the frequently occurring distortion of antenna pattern. Previous studies are mainly focused on the calibration by using the actual pattern or other assistant algorithms. In this paper, theoretical analysis is made to illustrate the reason for pattern distortion, which draws a conclusion that the distortion is essentially due to the different influence of environmental obstacles upon the electric field part and magnetic field part. This also implies that there is nearly no distortion between the two loops, which is validated by three different experiments. Based on this conclusion, the DOA estimation performance of MUSIC algorithm is studied by creating a numerical relationship associating the estimation error with distortion level. Furthermore, a new method using ideal pattern is proposed to improve the estimation performance by removing the amplitude information of monopole and relying on the crossed-loop processing. Both simulation and experimental results are given to prove its validity.

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.

Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the proposed algorithm, experimental data collected by the Ocean State Monitoring and Analyzing Radar, type SD (OSMAR-SD) were used, with the ship records from an automatic identification system (AIS) used as ground truth data. The processing results showed that the joint TF-CFAR method outperformed CFAR in detecting non-stationary and weak signals and those within the first-order sea clutters, whereas CFAR outperformed TF-CFAR in identifying multiple signals with similar frequencies. Notably, the intersection of the matched detection sets by TF-CFAR and CFAR alone was not immense, which takes up approximately 68% of the matched number by CFAR and 25% of that by TF-CFAR; however, the number in the union detection sets was much (>30%) greater than the result of either method. Therefore, joint detection with TF-CFAR and CFAR can further increase the detection probability and greatly improve detection performance under complicated situations, such as non-stationarity, low signal-to-noise ratio (SNR), and within the first-order sea clutters.