The use of autonomous vehicles for target localization in modern applications has emphasized their superior efficiency, improved safety, and cost advantages over human-operated methods. For localization tasks, autonomous vehicles can be used to increase efficiency and ensure that the target is localized as quickly and precisely as possible. However, devising a motion planning scheme to achieve these objectives in a computationally efficient manner suitable for real-time implementation is not straightforward. In this paper, we introduce a motion planning solution for enhanced target localization, leveraging Bernstein polynomial basis functions to approximate the probability distribution of the target's trajectory. This allows us to derive estimation performance criteria which are used by the motion planner to enhance the estimator efficacy. To conclude, we present simulation results that validate the effectiveness of the suggested algorithm.

翻译：自主车辆在现代化应用中的目标定位展现了相较于人工操作方法的卓越效率、更高的安全性以及成本优势。为完成定位任务，自主车辆可被用于提升效率，确保目标能够被尽可能快速且精确地定位。然而，设计一种能够实时实施且计算高效的运动规划方案来实现这些目标并非易事。本文提出了一种增强目标定位的运动规划解决方案，利用伯恩斯坦多项式基函数来近似目标轨迹的概率分布。这使得我们能够推导出用于运动规划器提升估计器效能的估计性能准则。最后，我们通过仿真结果验证了所提出算法的有效性。