通过引入方位角变化率提升目标运动估计精度，实现更敏捷目标的精准追踪。
A Cooperative Bearing-Rate Approach for Observability-Enhanced Target Motion Estimation
- 提出基于分布式递归最小二乘框架的协同估计算法STT-R，融合方位角变化率信息。
- 仿真与实验证明该算法显著降低速度估计滞后，提升对高机动目标的追踪能力。
- 适合需要高精度运动估计的无人机协同追踪、智能驾驶等场景。
基于视觉的目标运动估计是许多机器人任务的基础问题。现有方法因可观测性不足，在追踪高机动目标时面临挑战。针对空中目标追逐任务中目标可能在三维空间内剧烈机动的情况，本文研究如何通过引入文献中未充分探索的方位角变化率（bearing rate）信息来进一步增强可观测性。主要贡献是提出一种新的协同估计算法STT-R（空间-时间三角测量与方位角变化率），其设计基于分布式递归最小二乘框架。该理论结果通过数值仿真和真实实验得到验证。结果表明，所提出的STT-R算法能有效生成更精确的估计结果，并显著减少速度估计的滞后，从而实现对更敏捷目标的有效跟踪。
原文摘要 · Abstract (English)
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.
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