论文标题
基于点的价值迭代和线性高斯过程的大致最佳动态传感器选择
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes
论文作者
论文摘要
合成最佳传感器选择策略的问题与从事件检测到自动导航等各种工程应用程序有关。我们认为在无限的时间范围内具有折扣成本标准的这种综合问题。我们根据协方差矩阵的连续空间的价值迭代来提出这个问题。为了获得可计算处理的解决方案，我们随后制定了一个近似传感器选择问题，该问题可通过基于点的值迭代在有限的协方差矩阵的“网格”矩阵上解决，该迭代可解决，并具有用户定义的有界轨迹。我们提供了理论保证，可以通过此方法合成传感器选择策略的次优，并提供了比较它们与已知结果进行比较的数值示例。
The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation. We consider such a synthesis problem over an infinite time horizon with a discounted cost criterion. We formulate this problem in terms of a value iteration over the continuous space of covariance matrices. To obtain a computationally tractable solution, we subsequently formulate an approximate sensor selection problem, which is solvable through a point-based value iteration over a finite "mesh" of covariance matrices with a user-defined bounded trace. We provide theoretical guarantees bounding the suboptimality of the sensor selection policies synthesized through this method and provide numerical examples comparing them to known results.
