Radar Image-Based Positioning for USV Under GPS Denial Environment

Unmanned surface vehicle (USV) is an important application of unmanned systems, and these USVs provide safe and secure operation in hostile environments. But these USVs are highly reliant on their positioning system, such as global position system (GPS), and loss of positioning information from GPS can cause catastrophe. To overcome this positioning challenge for a USV under a GPS denial environment, we propose a real-time positioning algorithm based on radar and satellite images to determine the USV position. The algorithm takes coastline as a registration feature to implement an image registration between a horizontal viewing angle image from a radar and a vertical viewing angle image from a satellite. The contributions of this paper consist of two parts. First, a coastline feature extraction method based on edge gray features for both radar and satellite images is provided. Second, a high-efficiency image registration method, which takes the dimensionality reduction distance as an indicator, was proposed for the USV embedded system. The results from six typical application scenarios show that the maximum positioning error of the proposed algorithm is 28.02 m under the worst case. A continuous positioning experiment shows that the average error of the algorithm is 9.77 m, which indicates that the algorithm can meet the positioning requirements of a USV under GPS denial environment.