基于隐马尔科夫模型的中文术语识别研究

随着人工智能技术的发展，无人系统面临的任务愈发复杂，通常要求系统在未知环境下自主完成给定任务。为了解决无人系统在未知及不确定性问题方面的规划与决策，可以利用马尔科夫理论进行建模与估计，解决在无人平台中任务决策与规划、目标跟踪与识别，以及平台系统间通信等复杂问题。分别介绍马尔科夫理论在无人机（UAV）、无人车（UGV）以及自主式水下机器人（AUV）中的研究进展以及应用现状，指出当前存在的问题，并展望未来的发展趋势和研究热点。

Chinese term recognition based on hidden Markov model

With the development of artificial intelligence technology, the missions to be performed by unmanned systems are more and more complicated, and the systems are required to complete given missions independently in an unknown environment. In order to solve the planning and decision-making of unknown and uncertain problems in unmanned systems, Markov theory can be used for modeling and estimation to solve such complicated issues as mission decision-making and planning, target tracking and identification, and platform-to-system communication in unmanned platforms. This paper introduces the research progress and the practical application of Markov theory in Unmanned Aerial Vehicle(UAV), Unmanned Ground Vehicle(UGV)and Autonomous Underwater Vehicle(AUV)respectively, presents the current problems and the prospects of future development and research.