Priori Information-Based Feature Extraction Method for Small Target Detection in Sea Clutter

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 nonstationarity of sea clutter, it may lead to unstable extraction of detection features and then affect detection performance. To solve this problem, this article designs a feature extraction method based on a priori information for small target detection. It first obtains a priori information from historical echo data by the kernel density estimation (KDE) method. Then, the corresponding feature estimation method is utilized to obtain improved features 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 the next feature extraction. The measured data are utilized to verify the performance of the 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 the proposed method has certain application potential.