论文标题
导航的信息理论方法有效地检测和分类水下对象
Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects
论文作者
论文摘要
本文解决了一个自主探索问题，其中移动传感器放置在以前看不见的搜索区域中，利用信息理论导航成本功能来动态选择下一个接触操作，即从该操作中进行测量，以有效地检测和分类该区域内的感兴趣对象。本文提出的信息理论成本函数由两个\ textit {信息增益}术语组成，一个用于检测和定位对象，另一个用于对对象的顺序分类。我们为成本函数提出了一种新颖的封闭形式表示，该代表函数源自相互信息的定义。我们评估了选择下一个感应作用的三种不同政策：割草机，贪婪和非绿色。对于这三个政策，我们将信息理论成本功能的结果与其他信息启发的成本功能的结果进行了比较。我们的仿真结果表明，使用拟议的成本功能与其他方法相比，搜索效率更高，并且贪婪和非怪兽政策的表现优于草坪割草机政策。
This paper addresses an autonomous exploration problem in which a mobile sensor, placed in a previously unseen search area, utilizes an information-theoretic navigation cost function to dynamically select the next sensing action, i.e., location from which to take a measurement, to efficiently detect and classify objects of interest within the area. The information-theoretic cost function proposed in this paper consist of two \textit{information gain} terms, one for detection and localization of objects and the other for sequential classification of the detected objects. We present a novel closed-form representation for the cost function, derived from the definition of mutual information. We evaluate three different policies for choosing the next sensing action: lawn mower, greedy, and non-greedy. For these three policies, we compare the results from our information-theoretic cost functions to the results of other information-theoretic inspired cost functions. Our simulation results show that search efficiency is greater using the proposed cost functions compared to those of the other methods, and that the greedy and non-greedy policies outperform the lawn mower policy.
