Sea Surface Floating Small Target Detection Based on a Priori Feature Distribution and Multiscan Iteration

Abstract

To address the issue that the detection performance of conventional sea target detectors deteriorates seriously in short accumulated pulses, this article designs a feature detection method based on a priori feature distribution and multiscan iteration, which enhances the feature extraction ability of existing feature-based detection methods. The initial step involves the utilization of kernel density estimation for the purpose of fitting the a priori feature distribution model. Subsequently, the original feature vectors of the current scan are iterated based on the a priori feature distribution model to obtain improved feature vectors. After the feature iteration of the current scan is completed, the original feature vectors of the current scan are incorporated into the historical features to generate a new distribution model. The improved feature vectors after iteration are employed for training the decision region and detecting targets by the convex hull algorithm. The proposed method is designed to enhance the stability and reliability of detection features, thereby facilitating a greater degree of separation between the extracted features of sea clutter and target returns within the feature space. The measured IPIX data sets and Naval Aviation University X-Band data sets demonstrate that the proposed method can effectively improve the detection performance of existing multifeature-based detection methods in scenarios involving short accumulated pulses.