Floating Small Target Detection in Sea Clutter by One-Class SVM based on Three Detection Features

Abstract

In this paper, One-Class SVM (OCSVM) detector based on three detection features is proposed to distinguish floating small targets from sea clutter. Normalized Hurst exponent, relative Doppler peak height and relative Doppler vector entropy are extracted from radar returns as three detection features. Then, the separating hyperplane of OCSVM detector attending false alarm rate requirement is obtained by kernel trick of SVM. Therefore, the detection decision could be made according to the position of returns' features, which is familiar with outlier detection. The results of simulation experiments based on measured data sets show that the proposed detector acquires better detection performance compared with some existed detectors.