Aiming at the technical difficulty of marine radar to detect small targets embedded in the sea clutter, this article proposed a three-feature fusion detection method based on singular spectrum analysis. First, considering that the number of coherent pulses used by radar in scanning mode is usually small (64 or less), this method combines the application of radar historical scan data and current frame data, transfers the feature extraction method from intraframe to interframe, and extracts three features that consist of cumulative major singular value (CMSV), linear degree of second singular vector (LDSSV), and linear degree of third singular vector (LDTSV) from singular space of the cell under test (CUT). Second, in view of the unideal distribution of sea clutter samples, a 3-D concave hull learning algorithm based on the geometry shape of sea clutter samples under the framework of anomaly detection is developed by improving the original convex hull algorithm, and target detection is realized in feature space using this algorithm. Under the same parameter condition, the measured CSIR data verify the two following points: first, the performance of detector using concave hull learning algorithm is better than that of convex hull learning algorithm; second, the detection performance of the proposed detector is obviously better than that of tri-time–frequency (TF)-feature detector, trifeature-based detector, consistency factor detector, and fractal-based detector.

The target detection method based on multi-dimensional feature space is commonly used for detecting small maritime targets in sea clutter. However, existing methods do not fully explore and utilize the correlation information between features, limiting their detection performance. To address this limitation, this paper proposes a target detection method in sea clutter based on feature re-expression using Spearman correlation. First, a generalized linear grouping method based on Spearman correlation is proposed to strengthen the linear relationships within groups while reducing inter-group feature correlations. Second, intra-group feature re-expression based on Bhattacharyya distance is introduced, yielding new features with superior classification capabilities and complementary informational relationships. Third, a tri-feature concave hull detection algorithm based on dual distance weights is designed to enhance detection performance while controlling the false alarm rate. Testing with measured data from three public datasets demonstrates that the proposed detection method exhibits significant improvements in detection performance compared to existing feature-based detection methods.

Under the framework of feature-based detection of small targets on sea surface, existing feature extraction methods only use the echo data of current frame while ignoring the influence of historical echo data. Nevertheless, due to the non-stationarity of sea clutter, it may lead to unstable extraction of detection features, and then affect detection performance. To solve this problem, this paper designs a feature extraction method based on a priori information for small target detection. It firstly obtains a priori information from historical echo data by kernel density estimation (KDE) method. Then, the corresponding feature estimation method is utilized to obtain improved feature according to the relationship between current frame data and a priori information. Finally, the feature information of current frame is integrated into a priori information to prepare next feature extraction. Measured data are utilized to verify the performance of proposed method and the results reveal that, this method can effectively improve detection performance especially when sea clutter and target echo have good separability. In addition, the complexity of algorithm is analyzed to prove that proposed method has certain application potential.

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.

In strong sea clutter conditions, floating target echo signals are easily overwhelmed. Conventional mode decomposition and reconstruction methods struggle to reliably identify and select the modes that actually contain the target components. This paper proposes a spectral mode reconstruction method based on an adaptive selection criterion for target frequency intervals. Target modes are identified by combining the Doppler frequency shift of the target and the statistical spectral characteristics of sea clutter. An evaluation framework centered on relative feature gain and feature detection probability is then developed to validate the effectiveness of the proposed method. Experimental results on measured data demonstrate that the reconstructed signals significantly outperform the original signals on every metric evaluated, effectively suppressing sea clutter and enhancing target components.

Addressing the challenge of radar detection of small targets under sea clutter, target detection methods based on a three-dimensional feature space have shown effectiveness. However, their application has revealed several problems, including high dependency on linear relationships between features for dimensionality reduction, unclear reduction objectives, and spatial divergence of target samples, which limit detection performance. To mitigate these challenges, we constructed a feature density distance metric employing copula functions to quantitatively describe the classification capability of multidimensional features to distinguish targets from sea clutter. On the basis of this, a lightweight nonlinear dimensionality reduction network utilizing a self-attention mechanism was developed, optimally re-expressing multidimensional features into a three-dimensional feature space. Additionally, a concave hull classifier using feature sample distance was proposed to mitigate the negative impact of target sample divergence in the feature space. Furthermore, multivariate autoregressive prediction was used to optimize features, reducing erroneous decisions caused by anomalous feature samples. Experimental results using the measured data from the SDRDSP public dataset demonstrated that the proposed detection method achieved a detection probability more than 4% higher than comparative methods under Sea State 5, was less affected by false alarm rates, and exhibited superior detection performance under different false alarm probabilities from 10⁻³ to 10⁻¹.