Vision-based target motion estimation is a fundamental problem in many robotic tasks. The existing methods have the limitation of low observability and, hence, face challenges in tracking highly maneuverable targets. Motivated by the aerial target pursuit task where a target may maneuver in 3D space, this paper studies how to further enhance observability by incorporating the \emph{bearing rate} information that has not been well explored in the literature. The main contribution of this paper is to propose a new cooperative estimator called STT-R (Spatial-Temporal Triangulation with bearing Rate), which is designed under the framework of distributed recursive least squares. This theoretical result is further verified by numerical simulation and real-world experiments. It is shown that the proposed STT-R algorithm can effectively generate more accurate estimations and effectively reduce the lag in velocity estimation, enabling tracking of more maneuverable targets.

翻译：基于视觉的目标运动估计是众多机器人任务中的基础问题。现有方法存在观测性不足的局限性，因此在跟踪高机动性目标时面临挑战。受空中目标追捕任务的启发——目标可能在三维空间中进行机动，本文研究了如何通过引入文献中尚未充分探索的方位角变化率信息来进一步提升观测性。本文的主要贡献是提出了一种称为STT-R（融合方位角变化率的时空三角定位法）的新型协同估计器，该估计器基于分布式递归最小二乘框架设计。数值仿真和真实世界实验进一步验证了该理论结果。研究表明，所提出的STT-R算法能够有效生成更精确的估计，显著降低速度估计的滞后，从而实现对更高机动性目标的跟踪。