Design Considerations of Real-Time Radar Sensor Modeling for Unmanned Surface Vehicle (USV)

We present a design of the real-time radar sensor model for unmanned surface vehicles (USV). To construct an efficient learning environment of an unmanned surface vehicle (USV) for the swarm operation, accurate virtual modeling of the radar sensor with a light processing load is necessary. To achieve real-time modeling of the marine radar operations with a high level of modeling accuracy under a limited computational power, our work is to extract the signal-to-clutter noise ratio (SCNR) by considering physical radar specifications with pre-extracted target radar cross-section (RCS) using a 3D-EM simulator (HFSS). Modeling of various clutters such as rain, snow, fog as well as sea clutter has been carried out for each range bin with the generated clutter matrix with Rayleigh distribution. The standard deviations of the modeled clutter were calculated with widely adopted RCS estimation formulae. Also, the signal processing unit was modeled by implementing a cell average constant false alarm rate (CA-CFAR) engine to virtualize the signal processing effects of the physical radar on filtering backscattering clutters. The presented approach on maritime radar modeling can be useful in implementing a virtual environment with less computational complexity in developing various unmanned vehicles.