Maintaining consistent tracking of surface ships using unmanned surface vehicles (USVs) is particularly challenging due to the target's high maneuverability and complex motion trajectories. Additionally, environmental interferences in marine environments further complicate the task by reducing positioning accuracy. These issues often lead to tracking vibrations and delays, significantly impacting stability and precision. To address these challenges, this study proposes innovative solutions to improve the performance of USVs in maintaining effective target tracking.

This study presents an advanced bidirectional fitting algorithm integrating polynomial fitting and particle swarm optimization (PSO) to address radar sampling errors. By systematically analyzing the correlation between target motion amplitude and radar observation errors, the sampling period is optimized to accurately capture the target's motion patterns. Additionally, the optimal number of sampling points is carefully determined. Polynomial fitting is initially applied to minimize longitudinal errors, followed by secondary horizontal error reduction using PSO. A penalty function is incorporated to impose strict constraints on the fitting range, ensuring that corrected coordinates align with the actual motion capabilities of the target ship. Furthermore, real-time motion data from the USV and the target ship, including target speed, separation distance, and USV performance parameters, are used to develop a robust speed strategy. Geometric methods combined with USV turning dynamics are used to formulate a precise course strategy, enabling optimal speed and trajectory planning.

Rigorous validation through real-vessel experiments shows that the radar data processed with the bidirectional fitting algorithm achieves significantly enhanced smoothness, closely aligning with the ship's actual motion patterns.Moreover, the USV consistently and accurately tracks the target ship by following the optimized speed and course strategies. Vibrations and delays are effectively mitigated, ensuring stable tracking performance throughout the operation process.

The proposed method effectively addresses the challenges of stable target tracking for USVs, demonstrating exceptional performance in single-target scenarios. This work provides essential technical support and practical insights for advancing USV target tracking technology. Its findings provide reference value for further research and offer actionable guidance for broader applications in marine robotics and autonomous systems.