多站测距中的障碍物感知在线接收机规划
Obstacle-Aware Online Receiver Planning in Multistatic Ranging
AI总结：
针对多站测距中障碍物导致视距中断的问题，提出非近视后退时域框架，结合凸避障约束规划接收机轨迹，经数值实验验证其效率与跟踪精度。
AI中文摘要：
采用移动接收机的多站测距可凭借组合自适应传感器几何结构实现良好的跟踪性能，但存在信号遮挡障碍物的环境中，需规划接收机轨迹以保持与发射机及感兴趣目标的视距（LOS）条件。本文提出一种用于多站跟踪的非近视后退时域框架，其采用凸避障约束及聚焦于维持良好视距信号条件的控制目标，同时考虑未来障碍物情况，通过数值实验验证了该方法的效率与跟踪精度。
英文摘要：
Multistatic ranging with mobile receivers enables good tracking performance due to the combined adaptive sensor geometry. However, environments that contain signal obstructing obstacles require receiver trajectory planning to maintain line-of-sight (LOS) conditions with transmitters and the target of interest. In this letter, we develop a non-myopic receding-horizon framework for multistatic tracking. It uses convex collision-avoidance constraints and a control objective that focuses on maintaining good LOS signal conditions, taking into account future obstructions. We demonstrate the efficiency and tracking accuracy of the method via a numerical experiment.
