Small Target Detection on Sea Surface Based on Label Propagation Algorithm

Sea clutter and small targets have complex characteristics in the high-resolution radar system. For the target with small radar cross section, the traditional detection method has limited detection performance. In order to break through the critical signal to clutter ratio state, one or more features of radar echo can be extracted for joint feature detection, which is an important way to achieve effective detection in the case of critical signal to clutter ratio. At present, convex hull learning algorithm can be used to calculate the decision region and control effectively the false alarm probability in the feature space of three dimensions and below, but the computational complexity of convex hull learning algorithm is increased above the feature space, and make it difficult to detect target. To solve this problem, a small target detection method based on label propagation algorithm is proposed. It can detect small target in high-dimensional feature space and the false alarm can be effectively controlled. The experimental results on the actual database show, the detection probabilities of 88.4% and 92.0% are obtained in 0.512 s and 1.024 s respectively, which are 3.3% and 2.8% higher than those of the K-Nearest Neighbor (KNN) detector.