Reference source‐aided coordinate registration method for over‐the‐horizon radar using ensemble learning

Abstract

Since solving the path of radio wave propagation by an analytic method in the process of coordinate registration (CR) for skywave over-the-horizon radar (OTHR) is difficult, a reference source-aided CR method that uses ensemble learning is proposed. First, the OTHR wave propagation principle is briefly described, and the ionospheric channel characteristics implied by reference sources and target information are explained, which provides a theoretical basis for modelling. Then, multiple machine learning models, such as random forest, support vector machine and elastic network, are used to build a mapping network between known information and the actual ground distance of the targets. Moreover, the prediction ability and correlation of each machine learning model are evaluated. Finally, with stacking ensemble learning, machine learning models with better performance to solve CR problems are effectively combined as base learners. The models develop the advantages of each base learner to achieve a perception of the ionospheric environment and accurate OTHR CR. The simulation results show that the proposed method can mine the ionospheric channel propagation characteristics and effectively reduce the CR error of the targets of interest. When the information provided by 4 reference sources 200 km away from the target is used, the average error of target positioning is 13 km.