Abstract We address the general problem of multiple target localization and pursuit using measurements of the ranges from the targets to a set of autonomous pursuing vehicles, referred to as trackers. We develop a general framework for targets with models exhibiting uncertainty in the initial state, process, and measurement noise. The main objective is to compute optimal motions for the trackers that maximize the range-based information available for target localization and at the same time yield good target pursuit performance. The solution proposed is rooted in an estimation-theoretical setting that involves the computation of an appropriately defined Bayesian Fisher Information Matrix (FIM). The inverse of the latter yields a posterior Cramer–Rao Lower Bound (CRLB) on the covariance of the targets’ state estimation errors that can be possibly achieved with any estimator. Using the FIM, sufficient conditions on the trackers’ motions are derived for the ideal relative geometry between the trackers and the targets for which the range information acquired is maximal. This allows for an intuitive understanding of the types of ideal tracker trajectories. To deal with realistic constraints on the trackers’ motions and the requirement that the trackers pursue the targets, we then propose a model predictive control (MPC) framework for optimal tracker motion generation with a view to maximizing the predicted range information for target localization while taking explicitly into account the trackers’ dynamics, strict constraints on the trackers’ states and inputs, and prior knowledge about the targets’ states. The efficacy of the MPC is assessed in simulation through the help of representative examples motivated by operational scenarios involving single and multiple targets and trackers.

中文翻译：

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自动驾驶汽车基于距离的目标定位和追踪：一种使用后验 CRLB 和模型预测控制的方法

摘要我们使用从目标到一组自动追踪车辆（称为跟踪器）的范围的测量来解决多目标定位和追踪的一般问题。我们为目标开发了一个通用框架，其模型在初始状态、过程和测量噪声中表现出不确定性。主要目标是计算跟踪器的最佳运动，最大化可用于目标定位的基于距离的信息，同时产生良好的目标追踪性能。所提出的解决方案植根于估计理论设置，该设置涉及计算适当定义的贝叶斯费雪信息矩阵 (FIM)。后者的倒数在目标状态估计误差的协方差上产生后验 Cramer-Rao 下界（CRLB），这可以通过任何估计器实现。使用 FIM，可以为跟踪器和目标之间的理想相对几何形状导出跟踪器运动的充分条件，其中获取的距离信息是最大的。这允许对理想跟踪器轨迹的类型有直观的理解。为了处理跟踪器运动的现实约束和跟踪器追求目标的要求，我们提出了一个模型预测控制 (MPC) 框架，用于最佳跟踪器运动生成，以期最大化目标定位的预测范围信息，同时采取明确考虑跟踪器的动态，对跟踪器状态和输入的严格约束，以及关于目标状态的先验知识。MPC 的功效是在模拟中通过由涉及单个和多个目标和跟踪器的操作场景激发的代表性示例的帮助来评估的。