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The paper compared three reanalysis data by the air temperature and SST based on the observation data over the Jiangsu sea area. The results showed that the air temperature and SST of reanalysis data were highly consistent with the observation data, moreover, the consistency of SST was better. Among the three reanalysis data, the reliability of ERA5 was better than that of JRA-55 and NCEP. The consistency between the reanalysis data and the observation data was highest in Waikejiao observation station which was the farther offshore observation station. During periods of typhoon influence, the reanalysis data were closer to the observation data at Lyusi and Waikejiao observation station. At the Lianyungang observation station closer to the shore, there was a greater difference between the reanalysis data and the observation data. During the period of air temperature drop caused by cold air, the difference between reanalysis data and observation data was smaller. While during the temperature recovery period, the reanalysis data was significantly higher than the observation data at Lyusi and Waikejiao observation station. During the influence of cold air, the reduction of SST reflected by the reanalysis data was significantly lower than the observation data. The results provide baseline for the study on the applicability and reliability of reanalysis data in Jiangsu sea area.

Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.

Radar target detection in sea clutter is of significance to both civilian and military. With the miniaturization and invisibility of sea targets, floating small targets with slow speed have become the focus of radar detection. However, the detection of floating small targets in the background of sea clutter has always been a problem. Floating small targets usually have a weak Radar Cross Section (RCS) and slow speed, making it difficult to detect such targets in sea clutter. Traditional target detection methods exhibit poor performance in the detection of floating small targets. For the detection of small and weak targets on the sea surface, a high-Doppler-resolution and high-range-resolution system (double high system) is an effective way to solve this problem. In the double high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry and a subject of constant innovation. In recent years, under the double high system, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed a variety of feature-based target detection methods to alleviate the difficulty in detecting floating small targets when relying only on energy information and considerably improve detection performance. To ensure that relevant radar practitioners better understand the development and future trend of this field in recent years, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.