面向移动目标搜索的多星任务规划问题研究
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摘要
多星对地移动目标搜索问题具有重大的军事意义和民用价值,在态势评估、精确打击引导、海洋搜救和海关缉私等方面具有广阔的应用前景。采用多星对地移动目标进行搜索大大增加了卫星任务计划编制的难度,必须借助任务规划技术才能较好地管理和分配卫星资源。目前面向移动目标搜索的多星任务规划问题研究仍处于探索阶段,在理论研究和实际应用中存在许多亟待解决的问题。
基于上述背景,在总结分析国内外相关工作的基础上,论文针对面向移动目标搜索的多星任务规划问题展开研究,涉及的内容包括卫星任务规划问题形式化描述、问题模型设计及优化算法设计等方面,主要的工作及创新点如下:
(1)建立了面向移动目标搜索的多星任务规划问题的形式化描述,提出了面向移动目标搜索的多星任务规划的两种模式
深入分析卫星成像工作原理和卫星对移动目标搜索活动的业务流程,建立了移动目标及任务区域的形式化描述,并提出了卫星搜索候选行动集合的构造方法。在此基础上,对面向移动目标搜索的多星任务规划问题进行了阐述,分析了其中的约束条件和不确定性因素,并提出了两种规划模式:面向移动目标搜索的多星离线任务规划模式和面向移动目标搜索的多星在线规划模式。通过问题描述和规划模式划分,能够把握问题的主要矛盾和次要矛盾,为问题的建模和算法设计奠定了基础。
(2)建立了基于部分可观马尔可夫决策过程(POMDP)的面向移动目标搜索的多星离线任务规划模型
针对离线任务规划模式,在无侦察信息反馈条件下建立了未发现目标假设条件,依据该假设条件提出了移动目标分布离线更新和转移的方法,对未来时刻目标的分布进行估计,获得目标分布的先验概率。在目标分布先验概率的基础上,采用不确定序贯决策求解框架——部分可观马尔可夫决策过程(POMDP)建立了多星离线任务规划模型。模型以最大化累计探测概率为目标优化卫星的搜索活动,特点是在动态不确定环境下具有较强的适应性,求解的最优策略能够针对不同的初始先验分布制定卫星的最优搜索计划。
(3)提出了大地坐标系下目标运动预测算法
在线任务规划模式中,针对运动参数未知的移动目标,首先在平面笛卡尔坐标系下对目标的运动性质进行分析,推导出一种基于高斯分布的目标转移概率密度函数;然后在三维笛卡尔坐标系内进行扩展,得到地球表面上目标运动转移概率的数学描述;最后借助坐标转换原理和曲面积分方法,得到了大地坐标系下基于高斯分布的目标转移概率计算方法。该算法能够解决地面或者海面上移动目标的运动预测问题,并且避免了频繁的坐标转换,提高了计算的效率,同时在先验信息稀少、样本点不足的条件下也有较好的性能。
(4)建立了基于预测模型控制(MPC)的面向移动目标搜索的多星在线滚动任务规划模型
针对在线任务规划模式,根据侦察信息的反馈,采用预测模型控制理论和方法,建立了多星在线任务规划的闭环控制策略。该闭环中包括相互作用的目标预测模型和多星任务规划模型,其中目标预测模型利用大地坐标系下目标运动预测算法针对单先验点和双先验点模式,可以获得目标在未来时刻分布的先验概率;多星任务规划模型采用滚动优化的理念,将目标预测模型获取的先验概率作为反馈输入到后续的滚动窗口,在这些滚动窗口中以探测概率收益和搜索信息收益为优化目标,建立周期性滚动任务规划模型。通过闭环反馈控制机制和滚动窗口内反复进行的局部优化,达到在线规划卫星的目的,从而实现在整个任务时域上问题的求解。
Moving target search by multi-satellite is of great military and civilian value, which will be applied in situation assessment, strike navigation, maritime rescues and anti-smuggling. Since the complexity in making plans for moving target search, manual and single mode are no longer fit for the requirements. Mission planning is able to manage and allocate the satellite resources optimally. Till now multi-satellite mission planning for moving target search has been still in the initial stage with many open problems in theory and practice to be tackled.
This dissertation focuses on multi-satellite mission planning for moving target search, which includes formal problem description, planning mode, mathematic models, optimal algorithms and etc. The main work and contribution are as follows:
(1)The formal problem description and two planning modes are presented. Following through the analysis of satellite imaging procedure and operation flow of moving target search, the method for optional action set and the quantitive modeling of moving target and task region are reached. Based on above, the search problem, the constraints and uncertainty are analyzed. The on-line planning mode and off-line planning mode are also proposed. The formal description and the planning modes are the key to design mathematic models and algorithms.
(2)The POMDP model for multi-satellite off-line planning is presented. For off-line planning with no detection hypothesis, the method for moving target off-line update and transition is proposed to acquire target prior distribution probability. With the help of the prior distribution probability, the multi-satellite POMDP mission planning model is designed to maximize the detection probability sum. The model is adaptive to uncertainty which can transform different initial prior distribution probability into satellite search operation plans.
(3)Motion prediction algorithm in geodetic coordinate systems is proposed. Motion prediction in geodetic coordinate systems is well discussed. Firstly, moving target motion in plane Cartesian coordinate systems is analyzed and a Gaussian distribution of target transition probability density function (PDF) is deduced; Secondly, the PDF is extended to denote target transition probability in 3-D Cartesian coordinate; Thirdly, the method for computing the target transition probability in geodetic coordinate systems based on Gaussian distribution is deduced by coordinate systems transformation and curved surface integral method. The Motion prediction algorithm is able to avoid frequent coordinate transformation. It is still effective even with little prior information and sample points.
(4)The MPC model for multi-satellite on-line planning is presented. For on-line planning, the close-loop control architecture is acquired based on MPC. In the close-loop control architecture, the target prediction model and multi-satellite mission planning model are interactional. The target prediction model is utilized to get the target prior distribution in future by motion prediction algorithm. With the help of prior distribution, the multi-satellite mission planning model is to maximize the detection reward and information reward in each rolling window. The prediction model and the planning model are the key to predict the state of the system and to control optimally. By the iterative rolling optimization and state feedback in close-loop architecture, the global solution is obtained.
基于上述背景,在总结分析国内外相关工作的基础上,论文针对面向移动目标搜索的多星任务规划问题展开研究,涉及的内容包括卫星任务规划问题形式化描述、问题模型设计及优化算法设计等方面,主要的工作及创新点如下:
(1)建立了面向移动目标搜索的多星任务规划问题的形式化描述,提出了面向移动目标搜索的多星任务规划的两种模式
深入分析卫星成像工作原理和卫星对移动目标搜索活动的业务流程,建立了移动目标及任务区域的形式化描述,并提出了卫星搜索候选行动集合的构造方法。在此基础上,对面向移动目标搜索的多星任务规划问题进行了阐述,分析了其中的约束条件和不确定性因素,并提出了两种规划模式:面向移动目标搜索的多星离线任务规划模式和面向移动目标搜索的多星在线规划模式。通过问题描述和规划模式划分,能够把握问题的主要矛盾和次要矛盾,为问题的建模和算法设计奠定了基础。
(2)建立了基于部分可观马尔可夫决策过程(POMDP)的面向移动目标搜索的多星离线任务规划模型
针对离线任务规划模式,在无侦察信息反馈条件下建立了未发现目标假设条件,依据该假设条件提出了移动目标分布离线更新和转移的方法,对未来时刻目标的分布进行估计,获得目标分布的先验概率。在目标分布先验概率的基础上,采用不确定序贯决策求解框架——部分可观马尔可夫决策过程(POMDP)建立了多星离线任务规划模型。模型以最大化累计探测概率为目标优化卫星的搜索活动,特点是在动态不确定环境下具有较强的适应性,求解的最优策略能够针对不同的初始先验分布制定卫星的最优搜索计划。
(3)提出了大地坐标系下目标运动预测算法
在线任务规划模式中,针对运动参数未知的移动目标,首先在平面笛卡尔坐标系下对目标的运动性质进行分析,推导出一种基于高斯分布的目标转移概率密度函数;然后在三维笛卡尔坐标系内进行扩展,得到地球表面上目标运动转移概率的数学描述;最后借助坐标转换原理和曲面积分方法,得到了大地坐标系下基于高斯分布的目标转移概率计算方法。该算法能够解决地面或者海面上移动目标的运动预测问题,并且避免了频繁的坐标转换,提高了计算的效率,同时在先验信息稀少、样本点不足的条件下也有较好的性能。
(4)建立了基于预测模型控制(MPC)的面向移动目标搜索的多星在线滚动任务规划模型
针对在线任务规划模式,根据侦察信息的反馈,采用预测模型控制理论和方法,建立了多星在线任务规划的闭环控制策略。该闭环中包括相互作用的目标预测模型和多星任务规划模型,其中目标预测模型利用大地坐标系下目标运动预测算法针对单先验点和双先验点模式,可以获得目标在未来时刻分布的先验概率;多星任务规划模型采用滚动优化的理念,将目标预测模型获取的先验概率作为反馈输入到后续的滚动窗口,在这些滚动窗口中以探测概率收益和搜索信息收益为优化目标,建立周期性滚动任务规划模型。通过闭环反馈控制机制和滚动窗口内反复进行的局部优化,达到在线规划卫星的目的,从而实现在整个任务时域上问题的求解。
Moving target search by multi-satellite is of great military and civilian value, which will be applied in situation assessment, strike navigation, maritime rescues and anti-smuggling. Since the complexity in making plans for moving target search, manual and single mode are no longer fit for the requirements. Mission planning is able to manage and allocate the satellite resources optimally. Till now multi-satellite mission planning for moving target search has been still in the initial stage with many open problems in theory and practice to be tackled.
This dissertation focuses on multi-satellite mission planning for moving target search, which includes formal problem description, planning mode, mathematic models, optimal algorithms and etc. The main work and contribution are as follows:
(1)The formal problem description and two planning modes are presented. Following through the analysis of satellite imaging procedure and operation flow of moving target search, the method for optional action set and the quantitive modeling of moving target and task region are reached. Based on above, the search problem, the constraints and uncertainty are analyzed. The on-line planning mode and off-line planning mode are also proposed. The formal description and the planning modes are the key to design mathematic models and algorithms.
(2)The POMDP model for multi-satellite off-line planning is presented. For off-line planning with no detection hypothesis, the method for moving target off-line update and transition is proposed to acquire target prior distribution probability. With the help of the prior distribution probability, the multi-satellite POMDP mission planning model is designed to maximize the detection probability sum. The model is adaptive to uncertainty which can transform different initial prior distribution probability into satellite search operation plans.
(3)Motion prediction algorithm in geodetic coordinate systems is proposed. Motion prediction in geodetic coordinate systems is well discussed. Firstly, moving target motion in plane Cartesian coordinate systems is analyzed and a Gaussian distribution of target transition probability density function (PDF) is deduced; Secondly, the PDF is extended to denote target transition probability in 3-D Cartesian coordinate; Thirdly, the method for computing the target transition probability in geodetic coordinate systems based on Gaussian distribution is deduced by coordinate systems transformation and curved surface integral method. The Motion prediction algorithm is able to avoid frequent coordinate transformation. It is still effective even with little prior information and sample points.
(4)The MPC model for multi-satellite on-line planning is presented. For on-line planning, the close-loop control architecture is acquired based on MPC. In the close-loop control architecture, the target prediction model and multi-satellite mission planning model are interactional. The target prediction model is utilized to get the target prior distribution in future by motion prediction algorithm. With the help of prior distribution, the multi-satellite mission planning model is to maximize the detection reward and information reward in each rolling window. The prediction model and the planning model are the key to predict the state of the system and to control optimally. By the iterative rolling optimization and state feedback in close-loop architecture, the global solution is obtained.
引文
[1]黄汉文.卫星海洋目标监视系统分析与发展设想.装备指挥技术学院学报, 2004, 15 (5): 44-48.
[2]王勇刚,刘玉文.军事卫星及应用概论.北京:国防工业出版社, 2003.
[3]贺仁杰.成像侦察卫星调度问题研究.长沙:国防科技大学博士学位论文, 2004.
[4]阮启明.面向区域目标的成像侦察卫星调度问题研究.长沙:国防科技大学博士学位论文, 2006.
[5]闻新,杨嘉伟.军用卫星的发展趋势分析.现代防御技术, 2002, 30 (4): 7-11.
[6]魏晨曦,马婷婷.美国未来的侦察卫星及其关键技术.国际太空, 2005, 3: 22-27.
[7]梅国宝,吴世龙.电子侦察卫星的发展、应用及其面临的挑战.船舶电子对抗, 2005, 28 (4): 28-31.
[8]周彬.光学侦察卫星及反侦察技术综述.光电技术应用, 2004, 19 (5): 1-6.
[9]杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社, 2004.
[10] Lawrence A. Klein.多传感器数据融合理论及应用.北京:北京理工大学出版社, 2004.
[11] Steven P. Neeck, Thomas J. Magner, Granville E. Paules. NASA's Small Satellite Missions for Earth Observation. Acta Astronautica, 2005, 56 (1-2): 187-192.
[12] TerjeWahl, Gudrun K. Hoye, Aleksander Lyngvi, Bjorn T. Narheim. New possible roles of small satellites in maritime surveillance. Acta Astronautica, 2005, 56 (1-2): 273-277.
[13] Stuart J. Russell, Peter Norvig. Artificial Intelligence:A Modern Approach. New Jersey: Pearson Education, Inc., 2003.
[14]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究.长沙:国防科技大学博士学位论文, 2007.
[15]田菁.多无人机协同侦察任务规划问题建模与优化技术研究.长沙:国防科技大学博士学位论文, 2007.
[16]陈岩.蚁群优化理论在无人机战术控制中的应用研究.长沙:国防科技大学博士学位论文, 2007.
[17] Korf R. Real-Time Heuristic Search. Artificial Intelligence, 1990, 42 (2-3): 189-211.
[18]席裕庚.动态不确定环境下广义控制问题的预测控制.控制理论与应用, 2000, 17 (5): 665-670.
[19]张纯刚,席裕庚.机器人滚动路径规划的算法与仿真研究.高技术通讯, 2003, 13 (4): 53-57.
[20]席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划.自动化学报, 2002, 28 (2): 1-15.
[21] Zoran Sjanic. On-line Mission Planning Based on Model Predictive Control. Master's thesis,Link?ping University, 2001.
[22] Ning Xue, Zhen Ye. On-line UAV Route Replanning Based on Model Arithmetic Control. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, 2004.
[23] Lawrence D. Stone. Theory of Optimal Search. New York: Academic Press, 1975.
[24] Lawrence D. Stone. What's Happened in Search Theory since the 1975 Lanchester Prize. Operations Research, 1989, 37 (3): 501-506.
[25]朱清新.离散和连续空间中的最优搜索理论.北京:科学出版社, 2005.
[26]张宝康.搜索论的某些进展与应用.运筹学杂志, 1990, 9 (1): 31-38.
[27]吴晓锋.最优搜索理论的进展.运筹学杂志, 1992, 11 (2): 30-37.
[28]朱清新.最优搜索理论及其应用.世界科技研究与发展, 2005, 27 (4): 39-49.
[29] O.B. Koopman. Theory of Search, Pt. I. Kinematic Bases. Operations Research, 1956, 4 (3): 324-346.
[30] O.B. Koopman. Theory of Search, Pt. II. Target Detection. Operations Research, 1956, 4 (5): 503-531.
[31] O.B. Koopman. Theory of Search, Pt. III. The Optimum Distribution of Searching Effort. Operations Research, 1957, 5 (5): 613-626.
[32] Stephen M. Pollock. A Simple Model of Search for a Moving Target. Operations Research, 1970, 18 (5): 883-903.
[33] Lawrence D. Stone. Necessary and Sufficient Conditions for Optimal Search Plans for Moving Targets. Math.Opns. Res, 1979, 4 (4): 431-440.
[34] Washburn. On Search for a Moving Target. Naval Res. Logist Quart, 1979, 27: 315-322.
[35] Brown S. S. Optimal Search for a Moving Target in Discrete Time and Space. Opns. Res, 1980, 28 (6): 1275-1289.
[36] Trummekl K. E., J. R. Weisinger. The Complexity of the Optimal Searher Path Problem. Opns. Res, 1986, 34 (2): 324-327.
[37] James N. Eagle. The Optimal Search for a Moving Target When the Search PathIs Constrained. Operations Research, 1984, 32 (5): 1107-1115.
[38] Stone L. D, D. A. Davison, T. L. Corwin. Search And Localization Tactical Decision Aid. Metron Report to Lockheed-California Co, 1987.
[39] Yoshiaki Kuwata. Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control. Master's thesis,Massachusetts Institute of Technology, 2003.
[40] Chung Tin. Robust Multi-UAV Planning in Dynamic and Uncertain Environments. Master's thesis,Massachusetts Institute of Technology, 2004.
[41]周浦城,洪炳镕,蔡则苏.多机器人运动目标搜索策略研究.哈尔滨工业大学学报, 2005, 37 (7): 879-892.
[42] S BurrowBridge. Optimal Allocation of Satellite Networks Resource. Master's thesis,American Air Force Institute of Technology, 1999.
[43] Al Globus, James Crawford, Jason Lohn, Robert Morris. Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA, 2002.
[44] Jeremy Frank, Ari Jonsson, Robert Morris, David Smith. Planning and Scheduling for Fleets of Earth Observing Satellites. Proceeding of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002. Montreal, 2002.
[45]陈金勇,冯阳,彭会湘.一种卫星照相规划软件的可视化设计与实现.无线电通信技术, 2004, 30 (6): 35-37.
[46]陈站华.资源卫星任务安排测试方法的研究与实现.无线电工程, 2005, 35 (2): 62-64.
[47]代树武,孙辉先.卫星的智能规划与调度.控制与决策, 2003, 18 (2): 203-206.
[48]张帆.成像卫星计划编制中的约束建模及优化求解技术研究.长沙:国防科技大学博士学位论文, 2005.
[49]王军民.成像卫星鲁棒性调度方法及应用研究.长沙:国防科技大学博士学位论文, 2008.
[50]刘洋.成像侦察卫星动态重调度模型、算法及应用研究.长沙:国防科技大学博士学位论文, 2005.
[51]李菊芳.航天侦察多星多地面站任务规划问题研究.长沙:国防科技大学博士学位论文, 2005.
[52] J. Walton. Models for the management of satellite-based sensors. Ph.D. thesis,Massachusetts Institute of Technology, 1993.
[53] Ron Cohen. Automated Spacecraft Scheduling - the Aster Example. Jet propulsion Laboratory: Ground System Architectures Workshop, 2002.
[54] M. Lema?tre, G. Verfaillie. Daily Management of an Earth Observation Satellite: Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Third ILOG International Users Meeting. Paris, 1997.
[55] Michel Lema?tre, Gérard Verfaillie, Frank Jouhaud, Jean-Michel Lachiver. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002, 6 (5): 367-381.
[56] Mancel C. Complex Optimization Problems in Space Systems [EB/OL]. American Association for Aritficial intelligence ( www.aaai.org ), 2003.
[57] Claire Rivett, Carmine Pontecorvo. Improving Satellite Surveillance Through Optimal Assignment of Assets. Australian Government Department of Defence: DSTO-TR-1488, 2004.
[58]李曦.多星区域观测任务的效率优化方法研究.长沙:国防科技大学硕士学位论文, 2005.
[59] E. Bensana, G. Verfaillie. Exact and Inexact methods for the Daily Management of an Earth Observing Satellite. Proceedings of SpaceOPS. Munich, 1996.
[60] E Bensana, G Verfaillie, M Lemaitre. Earth Observing Satellite Management. Constraints, 1999, 4 (3): 293-299.
[61] Agnese J, Bataille N, Bensana E, Blumstein D. Exact and Approximate Methods for the Daily Management of an Earth Observation Satellite. Proceeding of the 5th ESA Workshop on Artificial Intelligence and Knowledge Based Systems for Space. 1995.
[62] T. Beker, G. Chechik, I. Meilijson, E. Ruppin. PlanSat– A Novel Solution for Imaging Satellite Scheduling. White paper. Soligence Corp.
[63] J Dungan, J Frank, A Jonsson, R Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites. Proceedings of the 2nd Annual Earth Science Technology Conference. Pasadena, California, 2002.
[64] G Verfaillie, E Bensana, C Michelon Edery, N Bataillie. Dealing with Uncertainty when Managing Earth Observing Satellites. Proceedings of i-SAIRAS-99. Netherlands. Noordwijk, 1999.
[65] G Verfaillie, Michel Lemaitre. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View. The Seventh International Conference on Practical and Principles of Constraint Programming. 2001.
[66] M Vasques, G Verfaillie, N Bataillie. Sharing the use of of a satellite: anoverview of methods. Proceeding of SpaceOPS. Tokyo, 1998.
[67] J. Wolfe. Willian, E. Sorensen. Stephen. Three scheduling algorithms applied to the earth observing systems domain. Management Science, 2000, 1.
[68] Al. Globus, J. Crawford, J. Lohn, A. Pryor. A Comparison of Techniques for Scheduling Earth Observing Satellites. AAAI 2004. 2004.
[69] L. Barbulescu, A. Howe, J.P. Watson, D. Whitley. Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search. PPSN 2002.
[70] Paul E. Berry, Carmine Pontecorvo, David A.B. Fogg. Optimal Search, Location and Tracking of Surface Maritime Targets by a Constellation of Surveillance Satellites. DSTO-TR-1480, 2002.
[71] X. rong Li, Vesselin P. Jilkov. Survey of Maneuvering Target Tracking Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39 (4): 1333-1364.
[72]周宏仁,敬忠良,王培德.机动目标跟踪.北京:国防工业出版社, 1991.
[73]蔡庆宇,薛毅,张伯彦.相控阵雷达数据处理及其仿真技术.北京:国防工业出版社, 1997.
[74]李新其,毕义明,李红霞.海上机动目标的运动预测模型及精度分析.火力与指挥控制, 2005, 30 (4): 35-37.
[75]李新其,毕义明,李红霞.海上机动目标群威胁预警建模及仿真实现.情报指挥控制系统与仿真技术, 2005, 27 (4): 28-32.
[76]方曼,张尚剑,陈德军,刘永智.可用于舰船运动预测的多项式拟合方法及参数选择.舰船科学技术, 2005, 27 (2): 24-26.
[77]谭守林,李新其,唐保国.组合建模的海上机动目标群威胁预警方法.火力与指挥控制, 2006, 31 (12 ): 83-86.
[78]谭守林,李新其,唐保国.组合建模的航母战斗群威胁预警方法.火力与指挥控制, 2007, 32 (3): 37-40.
[79] Ashraf Elnagar, Kamal Gupta. Motion Prediction of Moving Objects Based on Autoregressive Model. IEEE Transactions on Systems, Man and Cybernetics, 1998, 28 (6): 803-814.
[80] Allison Bruce, Geoffrey Gordon. Better Motion Prediction for People-tracking. In Proc. of the IEEE Int. Conf. on Robotics and Automation. New Orleans, 2004.
[81] Dizan Vasquez, Thierry Fraichard. Motion Prediction for Moving Objects:a Statistical Approach. Proceedings of the 2004 IEEE International Conference on Robotics & Automation. New Orleans, 2004.
[82]马志昊.天基空间监视雷达星座设计与任务规划研究.长沙:国防科技大学博士学位论文, 2007.
[83] Orbit Deep Space Image (ODSI). http://www.globalsecurity.org/space/systems/odsi.htm.
[84] Space Based Space Surveillance (SBSS). http://www.globalsecurity.org/space/systems/sbss.htm.
[85] Grant H. Stokes, Curt von Braun, Ramaswamy Sridharan, David Harrison. The Space-Based Visible Program. AIAA Space 2000 Conference & Exposition. Long Beach, 2000.
[86] Paul E Berry. A Dynamic Asset Tasking Technique for Integrated Surveillance Operations. DSTO-RR-0246, 2002.
[87] P.E Berry, D Fogg, C Pontecorvo. GAMBIT: Gauss-Markov and Bayesian Inference Technique for Information Uncertainty and Decision-Making in Surveillance Simulations. DSTO Research Report in draft.
[88]慈元卓,白保存,阮启明,谭跃进.多星侦察移动目标:一种基于潜在区域的求解策略.传感技术学报, 2008, 21 (6): 1015-1019.
[89]卢盼.面向海洋移动目标成像侦察任务的规划问题研究.长沙:国防科技大学硕士学位论文, 2007.
[90]姚怀钧.国外卫星军事应用技术发展概况.国际太空, 2000, 6: 20-22.
[91]朱述龙,张占睦.遥感图象获取与分析.北京:科学出版社, 2003.
[92]郑荣跃,王克昌,鄢小清.航天工程学.长沙:国防科技大学出版社, 1999.
[93]郗晓宁,王威,高玉东.近地航天器轨道基础.长沙:国防科技大学出版社, 2003.
[94] Mapping Asia with IKONOS 1-meter satellite imagery. http://www.blom.co.id/berita/Mapping%20Asia%2020%20July%202001.htm.
[95] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities. Proceeding of SpaceOPS. Tokyo, 1998.
[96] http://www.csrsr.ncu.edu.tw/tech-service/usermenu/six/C.1.html.
[97]寇连群.印度ResourceSat-1(P-1)卫星介绍.技术报告:中国科学院中国遥感卫星地面站, 2005.
[98] Wei-Cheng Lin, Da-Yin Liao, Chung-Yang Liu, Yung-Yao Lee. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE Transactions on Systems, Man and Cybernetics, 2004.
[99]何红艳,乌崇德,王小勇.侧摆对卫星及CCD相机系统参数的影响和分析.航天返回与遥感, 2003, 24 (4): 14-18.
[100] Michael Lederman Littman. Algorithms for Sequential Decision Making. Ph.D. thesis,Brown University, 1996.
[101] Huizhen Yu. Approximate Solution Methods for Partially Observable Markovand Semi-Markov Decision Processes. Ph.D. thesis,Massachusetts Institute of Technology, 2006.
[102] Edward J. Sondik. The Optimal Control of Partially Observable Markov Processes. Ph.D. thesis,Stanford University, 1971.
[103] Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence, 1998, 101 (1-2): 99-134.
[104] William S. Lovejoy. A Survey of Algorithmic Methods for Partially Observed Markov Decision Processes. Analysis of Operations Research, 1991, 28 (1-4): 47-66.
[105] Bellman R E. Dynamic Programming. New Jersey: Princeton University Press, 1957.
[106]刘克.实用马尔可夫决策过程.北京:清华大学出版社, 2004.
[107]《现代应用数学手册》编委会.现代应用数学册.概率统计与随机过程卷.北京:清华大学出版社, 2000.
[108] Howard R A. Dynamic Programming and Markov Processes. New York: Wiley, 1960.
[109] Richard D. Smallwood, Edward J. Sondik. The Optimal Control of Partially Observable Markov Processes Over a Finite Horizon. Operations Research, 1973, 21 (5): 1071-1088.
[110] Chelsea C. White III, William T. Scherer. Solution Procedures for Partially Observed Markov Decision Processes. Operations Research, 1989, 37 (5): 791-197.
[111] Anthony R. Cassandra. A Survey of POMDP Applications. In Proceedings of AAAI Fall Symposium on Planning with Partially Observable Markov Decision Processes. 1998.
[112]许博,吴敏.部分可观察马尔可夫决策过程研究进展.计算机工程与设计, 2007, 28 (9): 2116-2120.
[113] George E. Monahan. A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms. Management Science, 1982, 28 (1): 1-16.
[114] Adelita Posada-Bolivar. Partially Observed Markov Decision Process with Application. Ph.D. thesis,Toronto:Univsersity of Toronto, 1998.
[115] Mohammad Rezaeian. Sensor Scheduling for Optimal Observability Using Estimation Entropy. Proceedings of the Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. 2007.
[116] Ying He, Edwin K. P. Chong. Sensor Scheduling for Target Tracking in SensorNetworks. 43rd IEEE Conference on Decision and Control. Atlantis, 2004.
[117] Ying He, Edwin K.P. Chong. Sensor scheduling for target tracking:A Monte Carlo sampling approach. Digital Signal Processing, 2006, 16 (5): 533-545.
[118] Lyn C. Thomas, James N. Eagle. Criteria and Approximate Methods for Path-Constrained Moving-Target Search Problems. Naval Research Logistics, 1995, 42 (1): 27-38.
[119] Sumeetpal Singh, Vikram Krishnamurthy. The Optimal Search for a Markovian Target When the Search Path is Constrained: The Infinite-Horizon Case. IEEE Transactions on Automatic Control 2003, 48 (3): 493-497.
[120] WeiHong Zhang. Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,Hong Kong:The Hong Kong University of Science and Technology, 2001.
[121] Nevin L. Zhang, Weihong Zhang. Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes. Journal of Articial Intelligence Research, 2001, 14: 29-51.
[122] Masoumeh T. Izadi, Doina Precup, Danielle Azar. Belief Selection in Point-Based Planning Algorithms for POMDPs. Canadian AI 2006, LNAI 4013. Springer-Verlag, 2006.
[123] Anthony R. Cassandra. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,Rhode Island:Brown University, 1998.
[124] Nikos Vlassis, Matthijs T. J. Spaan. A Fast Point-based Algorithm for POMDPs. Proceedings of the Annual Machine Learning Conference of Belgium and the Netherlands. Brussels, 2004.
[125] Matthijs T. J. Spaan, Nikos Vlassis. Perseus: Randomized Point-based Value Iteration for POMDPs. Journal of Artificial Intelligence Research, 2005, 24: 195-220.
[126] Matthijs T. J. Spaan, Nikos Vlassis. A Point-based POMDP Algorithm for Robot Planning. In Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans, 2004.
[127] David Hsu, Wee Sun Lee, Nan Rong. A Point-Based POMDP Planner for Target Tracking. In Proc. IEEE Int. Conf. on Robotics & Automation. Pasadena, 2008.
[128] H. T. Cheng. Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,University of British Columbia, 1988.
[129] Joelle Pineau, Geoff Gordon, Sebastian Thrun. Point-based Value Iteration: An Anytime Algorithm for POMDPs. In Proc.Int.Joint Conf. on Artificial Intelligence. Acapulco, 2003.
[130]陈宝林.最优化理论与算法.北京:清华大学出版社, 2003.
[131]卞爱华,王崇骏,陈世福.基于点的POMDP算法的预处理方法.软件学报, 2008, 19 (6): 1309-1316.
[132] Milos Hauskrecht. Value-Function Approximations for Partially Observable Markov Decision Processes. Journal of Articial Intelligence Research, 2000, 13: 33-94.
[133]陈茂,陈小平.基于采样的POMDP近似算法.计算机仿真, 2006, 23 (5): 64-67.
[134]徐祖华.模型预测控制理论及应用研究.杭州:浙江大学博士学位论文, 2004.
[135]刘富春.多变量有约束模型预测控制算法及软件实现研究与应用.杭州:浙江大学博士学位论文, 2003.
[136]巢志骏.滚动规划和调度算法分析及复杂流水线调度问题的研究.上海:上海交通大学博士学位论文, 2003.
[137]李洪田.基于滚动优化的生产调度建模.济南:山东大学硕士学位论文, 2004.
[138] Yanli Yang, Ali A. Minai, Marios M. Polycarpou. Decentralized Cooperative Search in UAV's Using Opportunistic Learning. AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, 2002.
[139] Grcgory A. Mclntyre. A Comprehensive Approach to Sensor blanagement and Scheduling. Ph.D. thesis,George Mason University, 1998.
[140] Gregory A. McIntyre, Kenneth J. Hintz. An Information Theoretic Approach to Sensor Scheduling. SPIE on Signal Procssing, Sensor Fusion and Target Recognition. Orlando, 1996.
[141] Zhijun Tang. Information-theoretic Management of Mobile Sensor Agents. Ph.D. thesis,The Ohio State University, 2005.
[142]许国志.系统科学.上海:上海科技教育出版社, 2000.
[143] Ehsan S. Soofi. Capturing the Intangible Concept of Information. Journal of the American Statistical Association, 1994, 89 (428): 1243-1254.
[144]崔逊学.多目标进化算法及其应用.北京:国防工业出版社, 2006.
[145]宣家骥.多目标决策.长沙:湖南科学技术出版社, 1989.
[146]徐玖平,李军.多目标决策的理论与方法.北京:清华大学出版社, 2005.
[2]王勇刚,刘玉文.军事卫星及应用概论.北京:国防工业出版社, 2003.
[3]贺仁杰.成像侦察卫星调度问题研究.长沙:国防科技大学博士学位论文, 2004.
[4]阮启明.面向区域目标的成像侦察卫星调度问题研究.长沙:国防科技大学博士学位论文, 2006.
[5]闻新,杨嘉伟.军用卫星的发展趋势分析.现代防御技术, 2002, 30 (4): 7-11.
[6]魏晨曦,马婷婷.美国未来的侦察卫星及其关键技术.国际太空, 2005, 3: 22-27.
[7]梅国宝,吴世龙.电子侦察卫星的发展、应用及其面临的挑战.船舶电子对抗, 2005, 28 (4): 28-31.
[8]周彬.光学侦察卫星及反侦察技术综述.光电技术应用, 2004, 19 (5): 1-6.
[9]杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社, 2004.
[10] Lawrence A. Klein.多传感器数据融合理论及应用.北京:北京理工大学出版社, 2004.
[11] Steven P. Neeck, Thomas J. Magner, Granville E. Paules. NASA's Small Satellite Missions for Earth Observation. Acta Astronautica, 2005, 56 (1-2): 187-192.
[12] TerjeWahl, Gudrun K. Hoye, Aleksander Lyngvi, Bjorn T. Narheim. New possible roles of small satellites in maritime surveillance. Acta Astronautica, 2005, 56 (1-2): 273-277.
[13] Stuart J. Russell, Peter Norvig. Artificial Intelligence:A Modern Approach. New Jersey: Pearson Education, Inc., 2003.
[14]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究.长沙:国防科技大学博士学位论文, 2007.
[15]田菁.多无人机协同侦察任务规划问题建模与优化技术研究.长沙:国防科技大学博士学位论文, 2007.
[16]陈岩.蚁群优化理论在无人机战术控制中的应用研究.长沙:国防科技大学博士学位论文, 2007.
[17] Korf R. Real-Time Heuristic Search. Artificial Intelligence, 1990, 42 (2-3): 189-211.
[18]席裕庚.动态不确定环境下广义控制问题的预测控制.控制理论与应用, 2000, 17 (5): 665-670.
[19]张纯刚,席裕庚.机器人滚动路径规划的算法与仿真研究.高技术通讯, 2003, 13 (4): 53-57.
[20]席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划.自动化学报, 2002, 28 (2): 1-15.
[21] Zoran Sjanic. On-line Mission Planning Based on Model Predictive Control. Master's thesis,Link?ping University, 2001.
[22] Ning Xue, Zhen Ye. On-line UAV Route Replanning Based on Model Arithmetic Control. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, 2004.
[23] Lawrence D. Stone. Theory of Optimal Search. New York: Academic Press, 1975.
[24] Lawrence D. Stone. What's Happened in Search Theory since the 1975 Lanchester Prize. Operations Research, 1989, 37 (3): 501-506.
[25]朱清新.离散和连续空间中的最优搜索理论.北京:科学出版社, 2005.
[26]张宝康.搜索论的某些进展与应用.运筹学杂志, 1990, 9 (1): 31-38.
[27]吴晓锋.最优搜索理论的进展.运筹学杂志, 1992, 11 (2): 30-37.
[28]朱清新.最优搜索理论及其应用.世界科技研究与发展, 2005, 27 (4): 39-49.
[29] O.B. Koopman. Theory of Search, Pt. I. Kinematic Bases. Operations Research, 1956, 4 (3): 324-346.
[30] O.B. Koopman. Theory of Search, Pt. II. Target Detection. Operations Research, 1956, 4 (5): 503-531.
[31] O.B. Koopman. Theory of Search, Pt. III. The Optimum Distribution of Searching Effort. Operations Research, 1957, 5 (5): 613-626.
[32] Stephen M. Pollock. A Simple Model of Search for a Moving Target. Operations Research, 1970, 18 (5): 883-903.
[33] Lawrence D. Stone. Necessary and Sufficient Conditions for Optimal Search Plans for Moving Targets. Math.Opns. Res, 1979, 4 (4): 431-440.
[34] Washburn. On Search for a Moving Target. Naval Res. Logist Quart, 1979, 27: 315-322.
[35] Brown S. S. Optimal Search for a Moving Target in Discrete Time and Space. Opns. Res, 1980, 28 (6): 1275-1289.
[36] Trummekl K. E., J. R. Weisinger. The Complexity of the Optimal Searher Path Problem. Opns. Res, 1986, 34 (2): 324-327.
[37] James N. Eagle. The Optimal Search for a Moving Target When the Search PathIs Constrained. Operations Research, 1984, 32 (5): 1107-1115.
[38] Stone L. D, D. A. Davison, T. L. Corwin. Search And Localization Tactical Decision Aid. Metron Report to Lockheed-California Co, 1987.
[39] Yoshiaki Kuwata. Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control. Master's thesis,Massachusetts Institute of Technology, 2003.
[40] Chung Tin. Robust Multi-UAV Planning in Dynamic and Uncertain Environments. Master's thesis,Massachusetts Institute of Technology, 2004.
[41]周浦城,洪炳镕,蔡则苏.多机器人运动目标搜索策略研究.哈尔滨工业大学学报, 2005, 37 (7): 879-892.
[42] S BurrowBridge. Optimal Allocation of Satellite Networks Resource. Master's thesis,American Air Force Institute of Technology, 1999.
[43] Al Globus, James Crawford, Jason Lohn, Robert Morris. Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA, 2002.
[44] Jeremy Frank, Ari Jonsson, Robert Morris, David Smith. Planning and Scheduling for Fleets of Earth Observing Satellites. Proceeding of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002. Montreal, 2002.
[45]陈金勇,冯阳,彭会湘.一种卫星照相规划软件的可视化设计与实现.无线电通信技术, 2004, 30 (6): 35-37.
[46]陈站华.资源卫星任务安排测试方法的研究与实现.无线电工程, 2005, 35 (2): 62-64.
[47]代树武,孙辉先.卫星的智能规划与调度.控制与决策, 2003, 18 (2): 203-206.
[48]张帆.成像卫星计划编制中的约束建模及优化求解技术研究.长沙:国防科技大学博士学位论文, 2005.
[49]王军民.成像卫星鲁棒性调度方法及应用研究.长沙:国防科技大学博士学位论文, 2008.
[50]刘洋.成像侦察卫星动态重调度模型、算法及应用研究.长沙:国防科技大学博士学位论文, 2005.
[51]李菊芳.航天侦察多星多地面站任务规划问题研究.长沙:国防科技大学博士学位论文, 2005.
[52] J. Walton. Models for the management of satellite-based sensors. Ph.D. thesis,Massachusetts Institute of Technology, 1993.
[53] Ron Cohen. Automated Spacecraft Scheduling - the Aster Example. Jet propulsion Laboratory: Ground System Architectures Workshop, 2002.
[54] M. Lema?tre, G. Verfaillie. Daily Management of an Earth Observation Satellite: Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Third ILOG International Users Meeting. Paris, 1997.
[55] Michel Lema?tre, Gérard Verfaillie, Frank Jouhaud, Jean-Michel Lachiver. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002, 6 (5): 367-381.
[56] Mancel C. Complex Optimization Problems in Space Systems [EB/OL]. American Association for Aritficial intelligence ( www.aaai.org ), 2003.
[57] Claire Rivett, Carmine Pontecorvo. Improving Satellite Surveillance Through Optimal Assignment of Assets. Australian Government Department of Defence: DSTO-TR-1488, 2004.
[58]李曦.多星区域观测任务的效率优化方法研究.长沙:国防科技大学硕士学位论文, 2005.
[59] E. Bensana, G. Verfaillie. Exact and Inexact methods for the Daily Management of an Earth Observing Satellite. Proceedings of SpaceOPS. Munich, 1996.
[60] E Bensana, G Verfaillie, M Lemaitre. Earth Observing Satellite Management. Constraints, 1999, 4 (3): 293-299.
[61] Agnese J, Bataille N, Bensana E, Blumstein D. Exact and Approximate Methods for the Daily Management of an Earth Observation Satellite. Proceeding of the 5th ESA Workshop on Artificial Intelligence and Knowledge Based Systems for Space. 1995.
[62] T. Beker, G. Chechik, I. Meilijson, E. Ruppin. PlanSat– A Novel Solution for Imaging Satellite Scheduling. White paper. Soligence Corp.
[63] J Dungan, J Frank, A Jonsson, R Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites. Proceedings of the 2nd Annual Earth Science Technology Conference. Pasadena, California, 2002.
[64] G Verfaillie, E Bensana, C Michelon Edery, N Bataillie. Dealing with Uncertainty when Managing Earth Observing Satellites. Proceedings of i-SAIRAS-99. Netherlands. Noordwijk, 1999.
[65] G Verfaillie, Michel Lemaitre. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View. The Seventh International Conference on Practical and Principles of Constraint Programming. 2001.
[66] M Vasques, G Verfaillie, N Bataillie. Sharing the use of of a satellite: anoverview of methods. Proceeding of SpaceOPS. Tokyo, 1998.
[67] J. Wolfe. Willian, E. Sorensen. Stephen. Three scheduling algorithms applied to the earth observing systems domain. Management Science, 2000, 1.
[68] Al. Globus, J. Crawford, J. Lohn, A. Pryor. A Comparison of Techniques for Scheduling Earth Observing Satellites. AAAI 2004. 2004.
[69] L. Barbulescu, A. Howe, J.P. Watson, D. Whitley. Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search. PPSN 2002.
[70] Paul E. Berry, Carmine Pontecorvo, David A.B. Fogg. Optimal Search, Location and Tracking of Surface Maritime Targets by a Constellation of Surveillance Satellites. DSTO-TR-1480, 2002.
[71] X. rong Li, Vesselin P. Jilkov. Survey of Maneuvering Target Tracking Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39 (4): 1333-1364.
[72]周宏仁,敬忠良,王培德.机动目标跟踪.北京:国防工业出版社, 1991.
[73]蔡庆宇,薛毅,张伯彦.相控阵雷达数据处理及其仿真技术.北京:国防工业出版社, 1997.
[74]李新其,毕义明,李红霞.海上机动目标的运动预测模型及精度分析.火力与指挥控制, 2005, 30 (4): 35-37.
[75]李新其,毕义明,李红霞.海上机动目标群威胁预警建模及仿真实现.情报指挥控制系统与仿真技术, 2005, 27 (4): 28-32.
[76]方曼,张尚剑,陈德军,刘永智.可用于舰船运动预测的多项式拟合方法及参数选择.舰船科学技术, 2005, 27 (2): 24-26.
[77]谭守林,李新其,唐保国.组合建模的海上机动目标群威胁预警方法.火力与指挥控制, 2006, 31 (12 ): 83-86.
[78]谭守林,李新其,唐保国.组合建模的航母战斗群威胁预警方法.火力与指挥控制, 2007, 32 (3): 37-40.
[79] Ashraf Elnagar, Kamal Gupta. Motion Prediction of Moving Objects Based on Autoregressive Model. IEEE Transactions on Systems, Man and Cybernetics, 1998, 28 (6): 803-814.
[80] Allison Bruce, Geoffrey Gordon. Better Motion Prediction for People-tracking. In Proc. of the IEEE Int. Conf. on Robotics and Automation. New Orleans, 2004.
[81] Dizan Vasquez, Thierry Fraichard. Motion Prediction for Moving Objects:a Statistical Approach. Proceedings of the 2004 IEEE International Conference on Robotics & Automation. New Orleans, 2004.
[82]马志昊.天基空间监视雷达星座设计与任务规划研究.长沙:国防科技大学博士学位论文, 2007.
[83] Orbit Deep Space Image (ODSI). http://www.globalsecurity.org/space/systems/odsi.htm.
[84] Space Based Space Surveillance (SBSS). http://www.globalsecurity.org/space/systems/sbss.htm.
[85] Grant H. Stokes, Curt von Braun, Ramaswamy Sridharan, David Harrison. The Space-Based Visible Program. AIAA Space 2000 Conference & Exposition. Long Beach, 2000.
[86] Paul E Berry. A Dynamic Asset Tasking Technique for Integrated Surveillance Operations. DSTO-RR-0246, 2002.
[87] P.E Berry, D Fogg, C Pontecorvo. GAMBIT: Gauss-Markov and Bayesian Inference Technique for Information Uncertainty and Decision-Making in Surveillance Simulations. DSTO Research Report in draft.
[88]慈元卓,白保存,阮启明,谭跃进.多星侦察移动目标:一种基于潜在区域的求解策略.传感技术学报, 2008, 21 (6): 1015-1019.
[89]卢盼.面向海洋移动目标成像侦察任务的规划问题研究.长沙:国防科技大学硕士学位论文, 2007.
[90]姚怀钧.国外卫星军事应用技术发展概况.国际太空, 2000, 6: 20-22.
[91]朱述龙,张占睦.遥感图象获取与分析.北京:科学出版社, 2003.
[92]郑荣跃,王克昌,鄢小清.航天工程学.长沙:国防科技大学出版社, 1999.
[93]郗晓宁,王威,高玉东.近地航天器轨道基础.长沙:国防科技大学出版社, 2003.
[94] Mapping Asia with IKONOS 1-meter satellite imagery. http://www.blom.co.id/berita/Mapping%20Asia%2020%20July%202001.htm.
[95] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities. Proceeding of SpaceOPS. Tokyo, 1998.
[96] http://www.csrsr.ncu.edu.tw/tech-service/usermenu/six/C.1.html.
[97]寇连群.印度ResourceSat-1(P-1)卫星介绍.技术报告:中国科学院中国遥感卫星地面站, 2005.
[98] Wei-Cheng Lin, Da-Yin Liao, Chung-Yang Liu, Yung-Yao Lee. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE Transactions on Systems, Man and Cybernetics, 2004.
[99]何红艳,乌崇德,王小勇.侧摆对卫星及CCD相机系统参数的影响和分析.航天返回与遥感, 2003, 24 (4): 14-18.
[100] Michael Lederman Littman. Algorithms for Sequential Decision Making. Ph.D. thesis,Brown University, 1996.
[101] Huizhen Yu. Approximate Solution Methods for Partially Observable Markovand Semi-Markov Decision Processes. Ph.D. thesis,Massachusetts Institute of Technology, 2006.
[102] Edward J. Sondik. The Optimal Control of Partially Observable Markov Processes. Ph.D. thesis,Stanford University, 1971.
[103] Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence, 1998, 101 (1-2): 99-134.
[104] William S. Lovejoy. A Survey of Algorithmic Methods for Partially Observed Markov Decision Processes. Analysis of Operations Research, 1991, 28 (1-4): 47-66.
[105] Bellman R E. Dynamic Programming. New Jersey: Princeton University Press, 1957.
[106]刘克.实用马尔可夫决策过程.北京:清华大学出版社, 2004.
[107]《现代应用数学手册》编委会.现代应用数学册.概率统计与随机过程卷.北京:清华大学出版社, 2000.
[108] Howard R A. Dynamic Programming and Markov Processes. New York: Wiley, 1960.
[109] Richard D. Smallwood, Edward J. Sondik. The Optimal Control of Partially Observable Markov Processes Over a Finite Horizon. Operations Research, 1973, 21 (5): 1071-1088.
[110] Chelsea C. White III, William T. Scherer. Solution Procedures for Partially Observed Markov Decision Processes. Operations Research, 1989, 37 (5): 791-197.
[111] Anthony R. Cassandra. A Survey of POMDP Applications. In Proceedings of AAAI Fall Symposium on Planning with Partially Observable Markov Decision Processes. 1998.
[112]许博,吴敏.部分可观察马尔可夫决策过程研究进展.计算机工程与设计, 2007, 28 (9): 2116-2120.
[113] George E. Monahan. A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms. Management Science, 1982, 28 (1): 1-16.
[114] Adelita Posada-Bolivar. Partially Observed Markov Decision Process with Application. Ph.D. thesis,Toronto:Univsersity of Toronto, 1998.
[115] Mohammad Rezaeian. Sensor Scheduling for Optimal Observability Using Estimation Entropy. Proceedings of the Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. 2007.
[116] Ying He, Edwin K. P. Chong. Sensor Scheduling for Target Tracking in SensorNetworks. 43rd IEEE Conference on Decision and Control. Atlantis, 2004.
[117] Ying He, Edwin K.P. Chong. Sensor scheduling for target tracking:A Monte Carlo sampling approach. Digital Signal Processing, 2006, 16 (5): 533-545.
[118] Lyn C. Thomas, James N. Eagle. Criteria and Approximate Methods for Path-Constrained Moving-Target Search Problems. Naval Research Logistics, 1995, 42 (1): 27-38.
[119] Sumeetpal Singh, Vikram Krishnamurthy. The Optimal Search for a Markovian Target When the Search Path is Constrained: The Infinite-Horizon Case. IEEE Transactions on Automatic Control 2003, 48 (3): 493-497.
[120] WeiHong Zhang. Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,Hong Kong:The Hong Kong University of Science and Technology, 2001.
[121] Nevin L. Zhang, Weihong Zhang. Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes. Journal of Articial Intelligence Research, 2001, 14: 29-51.
[122] Masoumeh T. Izadi, Doina Precup, Danielle Azar. Belief Selection in Point-Based Planning Algorithms for POMDPs. Canadian AI 2006, LNAI 4013. Springer-Verlag, 2006.
[123] Anthony R. Cassandra. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,Rhode Island:Brown University, 1998.
[124] Nikos Vlassis, Matthijs T. J. Spaan. A Fast Point-based Algorithm for POMDPs. Proceedings of the Annual Machine Learning Conference of Belgium and the Netherlands. Brussels, 2004.
[125] Matthijs T. J. Spaan, Nikos Vlassis. Perseus: Randomized Point-based Value Iteration for POMDPs. Journal of Artificial Intelligence Research, 2005, 24: 195-220.
[126] Matthijs T. J. Spaan, Nikos Vlassis. A Point-based POMDP Algorithm for Robot Planning. In Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans, 2004.
[127] David Hsu, Wee Sun Lee, Nan Rong. A Point-Based POMDP Planner for Target Tracking. In Proc. IEEE Int. Conf. on Robotics & Automation. Pasadena, 2008.
[128] H. T. Cheng. Algorithms for Partially Observable Markov Decision Processes. Ph.D. thesis,University of British Columbia, 1988.
[129] Joelle Pineau, Geoff Gordon, Sebastian Thrun. Point-based Value Iteration: An Anytime Algorithm for POMDPs. In Proc.Int.Joint Conf. on Artificial Intelligence. Acapulco, 2003.
[130]陈宝林.最优化理论与算法.北京:清华大学出版社, 2003.
[131]卞爱华,王崇骏,陈世福.基于点的POMDP算法的预处理方法.软件学报, 2008, 19 (6): 1309-1316.
[132] Milos Hauskrecht. Value-Function Approximations for Partially Observable Markov Decision Processes. Journal of Articial Intelligence Research, 2000, 13: 33-94.
[133]陈茂,陈小平.基于采样的POMDP近似算法.计算机仿真, 2006, 23 (5): 64-67.
[134]徐祖华.模型预测控制理论及应用研究.杭州:浙江大学博士学位论文, 2004.
[135]刘富春.多变量有约束模型预测控制算法及软件实现研究与应用.杭州:浙江大学博士学位论文, 2003.
[136]巢志骏.滚动规划和调度算法分析及复杂流水线调度问题的研究.上海:上海交通大学博士学位论文, 2003.
[137]李洪田.基于滚动优化的生产调度建模.济南:山东大学硕士学位论文, 2004.
[138] Yanli Yang, Ali A. Minai, Marios M. Polycarpou. Decentralized Cooperative Search in UAV's Using Opportunistic Learning. AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, 2002.
[139] Grcgory A. Mclntyre. A Comprehensive Approach to Sensor blanagement and Scheduling. Ph.D. thesis,George Mason University, 1998.
[140] Gregory A. McIntyre, Kenneth J. Hintz. An Information Theoretic Approach to Sensor Scheduling. SPIE on Signal Procssing, Sensor Fusion and Target Recognition. Orlando, 1996.
[141] Zhijun Tang. Information-theoretic Management of Mobile Sensor Agents. Ph.D. thesis,The Ohio State University, 2005.
[142]许国志.系统科学.上海:上海科技教育出版社, 2000.
[143] Ehsan S. Soofi. Capturing the Intangible Concept of Information. Journal of the American Statistical Association, 1994, 89 (428): 1243-1254.
[144]崔逊学.多目标进化算法及其应用.北京:国防工业出版社, 2006.
[145]宣家骥.多目标决策.长沙:湖南科学技术出版社, 1989.
[146]徐玖平,李军.多目标决策的理论与方法.北京:清华大学出版社, 2005.