A Method for Detecting Small Targets in Sea Surface Based on Singular Spectrum Analysis

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.