Hierarchical markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications
Hierarchical markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications
复制标题
基于分布式数据融合和协作传感器管理的分层马尔可夫决策过程，用于多目标多传感器跟踪应用
DOI：
10.1109/icsmc.2007.4413675
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
T. Kirubarajan
中科院分区：
文献类型：
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作者：
D. Akselrod;A. Sinha;T. Kirubarajan
本文提出了一种基于层次马尔可夫决策过程的决策机制，解决了多目标多传感器跟踪中的分布式数据融合和传感器协同管理两个重要问题。将其应用于分布式数据融合中，提出了一种层次化、多层次的协同分布式数据融合决策机制，为各个平台提供融合过程所需的数据，同时大大减少了整个系统信息流中的冗余。我们考虑一个分布式数据融合系统，它由分散的、异构的、可能不可靠的平台组成。在传感器协同管理的应用中，研究了在包含多个运动目标的区域内执行监视任务的多架无人机的分散协同控制问题。目标是最大限度地获得信息，以最大可能的精度跟踪尽可能多的目标。每架无人机获得的关于地面目标位置的信息的不确定性在问题表述中得到了解决。仿真示例演示了该方法的操作和性能结果。
This paper presents a decision mechanism based on hierarchical Markov decision processes as a solution for two important problems in multitarget multisensor tracking - distributed data fusion and collaborative sensor management. In application to the distributed data fusion, this paper presents a hierarchical multi-level decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. In application to collaborative sensor management, this paper studies the problem of decentralized cooperative control of a group of unmanned aerial vehicles (UAVs) carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. Simulation examples demonstrate the operation and the performance results.