Feature-based target detection methods are predominantly used to determine the presence or absence of detection targets under sea clutter conditions, but they exhibit a deficiency in making nuanced classification judgments for the different categories of detected targets. Both ship targets and marine surface floating small objects display specific sea clutter characteristics to various degrees. Given the periodic consistency of transient power and the Doppler centroid bandwidth observed in sea clutter, this article examines the manifestation level of this characteristic in both categories of targets, drawing on their respective motion mechanisms. The ability to distinguish between these two categories of targets using this characteristic has been validated through the analysis of empirical data, subsequently leading to the formulation of discriminant statistics that facilitate target classification. The data confirm the effectiveness of this approach, illustrating its robust classification performance.

Sea surface non-stationarity poses significant challenges to sea-surface small target detection, particularly in maintaining a stable false alarm rate (FAR). In dynamic maritime scenarios with non-stationary characteristics, the non-stationarity of sea clutter can easily cause significant changes in the clutter feature space, leading to a notable deviation between the preset FAR and the measured FAR. By analyzing the temporal and spatial variations in sea clutter, we model the relationship between the preset FAR and the measured FAR as a two-parameter linear function. To address the impact of sea surface non-stationarity on FAR, the model parameters are estimated in real time within the environment and used to guide the dynamic adjustment of the decision region. We applied the proposed method to both convex hull and support vector machine (SVM) detectors and conducted experiments using measured X-band sea-detecting datasets. Experiments demonstrate that the proposed method effectively reduces the deviation between the measured mean FAR and the preset FAR. When the preset FAR is 10⁻², the proposed method achieves an average FAR of 1.067 × 10⁻² with the convex hull detector and 1.043 × 10⁻² with the SVM detector.

In addressing the issue of weak target detection at sea, most existing feature detection methods are designed for scenarios with low sea states and small grazing angles. Under high sea states and large grazing angles, variations in scattering mechanisms lead to changes in feature characteristics, resulting in performance degradation when these methods are applied directly due to scene mismatch. To address this, this paper employs four quantitative metrics—mean feature value, coefficient of variation, Bhattacharyya distance, and Spearman correlation coefficient—to analyze the centrality, variability, separability, and correlations of nine features in the time, frequency, and time-frequency domains under varying sea states and grazing angles. The study reveals that, with increasing sea state and changing grazing angles, the separability of time-frequency features, especially the time-frequency ridge accumulation, declines more gradually than other features, and feature correlations generally weaken. These findings provide a reference for joint feature detection in complex scenarios. To optimize feature application, the Spearman correlation coefficient matrix was transformed into a generalized distance matrix, and spectral clustering was used to group features with strong correlations. Feature selection was then performed from the clusters based on mean feature value, coefficient of variation, and Bhattacharyya distance, yielding an optimal feature set for the current scenario. Validation on the SDRDSP dataset under sea states 4–5 showed that the proposed method achieved an average detection probability 10.64% higher than existing methods. Further validation on the Yantai angle airborne test dataset, with grazing angles ranging from 62° to 82°, showed an average detection probability increase of 10.07% over existing methods.