Detection of Small Sea-surface Target Based on Random Forest in High-dimensional Feature Domain

Feature detection is an effective way to improve the detection of small sea-surface targets. Aiming at the problems of low detection probability of low-dimensional features and difficult control of high-dimensional feature false alarms, a high-dimensional feature detection method based on random forest with controllable false alarms is proposed in this paper. First, multi-dimensional features are extracted from multiple domains of time domain, frequency domain, and time-frequency domain. The detection problem is converted into a two-class classification problem in high-dimensional feature space. Second, two types of balanced training samples including sea clutter and target echo are obtained by simulating returns with target. Third, random forest algorithm is introduced into high-dimensional feature space, and function expression of the splitting factor and the false alarm rate is established to obtain the control region of false alarm. Finally, it is verified by the IPIX measured data that the proposed detector has a certain performance improvement and meets the requirements of real radar with constant false alarm detection.