With the rapid development of artificial intelligence, maritime technology is continuously advancing, and unmanned surface vessels (USV) are gaining significant attention as emerging waterborne transportation vehicles. Environmental monitoring during the navigation of USV is a critical component. This paper employs multiple millimeter-wave radars and utilizes a multi-hypothesis tracking algorithm to successfully achieve data association, data fusion, and target tracking for millimeter-wave radar data. Finally, the proposed method is validated through real ship experiments, providing a solid theoretical foundation for autonomous navigation of USV in the future.