多类型对地观测卫星联合任务规划关键技术研究
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摘要
对地观测卫星利用卫星传感器从太空对地球表面进行探测以获取信息,已广泛应用于社会经济和军事等领域。随着在轨卫星类型增加,以及人们对对地观测数据的需求类型日益复杂化,越来越需要对多类型对地观测卫星进行联合任务规划。目前关于卫星任务规划的研究大多针对单类型卫星、面向简单观测任务请求展开,考虑的卫星观测过程和载荷约束相对简化,缺乏面向多类型卫星、多类型观测任务请求、将卫星观测与数据传输联合考虑的方法和技术。
论文面向光学、SAR、电子侦察等多种类型的中低轨对地观测卫星联合任务规划的应用需求,以多类型对地观测卫星和多类型数传资源的联合为手段,以满足多种类型的观测任务请求为出发点,在总结分析国内外相关研究工作的基础上,对多类型对地观测卫星联合任务规划关键问题进行研究。主要工作和创新点包括:
(1)建立了多类型对地观测卫星联合任务规划层次化描述模型。由于多类型卫星联合任务规划问题的复杂性,论文系统地分析和总结了多类型卫星联合任务规划的规划要素,针对参与规划卫星载荷多样和观测任务请求多样的特点,将问题模型抽象为五个层次,并重点对资源约束、规划约束、业务组件、业务过程四个层次进行详细地建模描述;论文模型采用了组件式建模方法,约束和优化目标函数作为组件参与到模型中,在规划过程中可以根据观测任务请求特点、优化策略和规划要素约束特点动态地选择适宜的处理流程,每种流程又可以根据问题特点进行约束和优化目标函数的组合选择,从而使问题模型具有高度的可移植性和扩展性,能够较好地适应多类型卫星联合任务规划的复杂规划要求。
(2)提出了数传资源规划数学模型和优化算法。作为卫星任务规划的重要组成部分,数传资源规划对卫星动作规划具有重要影响,需要展开研究。论文以卫星观测过程中星载存储器变化为研究切入点,建立了基于约束图的数传资源规划模型,将数传资源规划问题转化为约束图中多顶点对之间的多条路径搜索问题;并基于置换序列表示方法,引入自适应邻域搜索机制以改进邻域搜索过程,提出了自适应邻域搜索模拟退火算法,算法收敛性分析和实验验证了算法的优化性。相对于其它方法,论文模型从星地联合的角度出发,考虑了观测任务分布、卫星载荷能力、卫星实传和回放动作对规划的影响,因而能够更贴切地满足对地观测卫星的数传需求,论文算法相对于模拟退火、随机爬山和遗传算法等算法也具有更好的全局收敛性。
(3)面向综合效益优先策略提出了基于贪婪随机自适应搜索过程算法框架的混合算法。面向综合效益优先策略的多类型对地观测卫星联合任务规划问题具有多时间窗约束车辆装卸和过载规划问题的问题特点。论文采用修复搜索问题求解机制,提出了一种新的混合算法,该算法基于贪婪随机自适应搜索过程求解框架,在构建阶段引入大邻域搜索算法,并在大邻域搜索结果保留和每一步的迭代搜索中分别应用模拟退火思想,算法同时将车辆装卸问题的三种邻域算子高效组合,提出一种新的迭代修复邻域搜索算子以改进算法搜索过程。算法收敛性分析验证了算法的全局收敛性,实验验证结果也表明算法具有更好的收敛性;该算法可对车辆装卸问题和过载规划问题提供一种新的求解思路。
(4)面向任务优先策略提出了分层控制免疫遗传算法。任务优先下多类型对地观测卫星联合任务规划的观测任务子任务之间具有复杂关联关系,使问题成为一个复杂的约束优化问题。论文基于免疫遗传算法框架,提出了分层控制免疫遗传算法;算法采用置换序列编码和双层编码方式,求解中引入分层控制机制,对顶层编码采用遗传操作算子,对底层编码采用免疫操作算子,并在免疫操作算子中引入免疫选择、免疫更新、小生境等思想以改进搜索过程,有效解决任务优先策略下的多类型卫星联合任务规划问题。实验结果表明该算法在组合观测任务参与规划的算例下是可行有效的。
(5)面向移动跟踪监视任务提出了多类型对地观测卫星联合任务规划流程和算法。针对移动跟踪监视任务,根据获取的先验信息的不同,提出了搜索发现+跟踪监视配合的任务规划流程:无先验信息条件下,即搜索发现阶段,对区域进行等距网格划分,假设目标位置在搜索区域内随机分布,目标运动模型为随机运动,提出了基于网格目标分布概率动态更新的搜索发现算法;部分先验信息已知条件下,即跟踪监视阶段,考虑地理信息和目标先验信息对目标运动的影响,借鉴交互多模型算法思想,引入目标运动模型的自适应更新和选择机制,提出自适应交互多模型移动目标跟踪监视算法,解决了面向移动跟踪监视任务的多类型卫星联合任务规划问题。实验验证表明上述流程和算法是可行有效的。
Earth Observing Satellites (EOSs) obtain information of the earth's surface from outer space by using satellite sensors. They are widely used in the social, economic, and military applications. In recent years, as more and more EOSs are launched to meet the needs of various complicated types of remote sensing data, the challenge to scheduling of multiple types of EOSs is increased. However, current researches focus on single type of satellite and simple requirements, which considers relatively simple earth observing procedure and restrictions, and lacks of methods and techniques for scheduling of multiple types of satellites, satisfying multiple types of requirements and combining satellite observing with data transmitting.
To satisfy the united scheduling requirements of multiple types of low orbit satellites, including optical satellites, SAR satellites, electronic satellites, and ocean surveillance satellites, this dissertation studies the key technologies of the united scheduling of multiple types of EOSs. Research focus is put onto combining multiple types of EOSs with multiple types of data transmitting resources, thus to meet the needs of various earth observing tasks. The main work and contributions of this dissertation can be concluded as the following five parts:
(1) We build a hierarchical description model for united scheduling of multiple types of EOSs. Due to the complexity of the united scheduling of multiple types of EOSs, the dissertation systematically analyzes and summaries the scheduling features in the target problem. Focusing on the characteristics of the scheduling workload and the diversity of the observing tasks, the dissertation concludes the problem model into five levels, and put focuses on four levels: resource constraints, scheduling constraints, business component and business process, meanwhile gives detailed description of the modeling process. The dissertation uses a component based modeling methodology to build the description model. Therefore, both the constraints and the optimization target are taken as components in the model. In the scheduling process, it is convenient to choose suitable process according to different characteristics of the observing tasks, the optimization strategy and the scheduling features. Moreover, during each process, the constraints and the optimization target functions can be combined dynamically according to different problem features. All of which make the description model highly compatible and extensible, thus could satisfy the complicated requirements of the united scheduling of multiple types of EOSs .
(2) We propose the techniques to the scheduling of various data transmitting resources from both the satellites and ground stations. Results of the scheduling of data transmitting play an important role in satellite scheduling; therefore it must be studied first. Considering the variation of the on-satellite memorizer during the earth observing, the dissertation establishes a scheduling model for data transmitting resources based on the constraint graph, which transforms the scheduling problem into a multiple paths searching problem. By using the permutation based representation, an adaptive neighborhood searching mechanism is introduced to improve the neighborhood searching process. Moreover, a simulated annealing algorithm based on the adaptive neighborhood searching is proposed. Analysis to the convergence of the algorithm and the experimental results verify that the proposed model and algorithm are superior. Compared to other algorithms, the proposed method synthetically considers the following factors: the distribution of the earth observing tasks, the capability of the satellites' load, the effects of the real time transmission and storage data transmission to the scheduling. Therefore, the method could satisfy the data transmitting requirements more suitably. Moreover, compared to the simulated annealing algorithm, the stochastic hill-climbing algorithm and the genetic algorithm, the proposed method maintains much better global convergence.
(3) We present a hybrid algorithm based on the GRASP (Greedy Randomized Adaptive Search Procedure, GRASP) framework for Benefit First strategy. The united scheduling problem has the similar characteristics with both the PDPMTW (Pickup and Delivery Problem with Multiple Time Windows) and oversubscribed scheduling problem. By using the mechanism of the repair search, the dissertation proposes a new hybrid algorithm which is based on the GRASP framework. The large neighborhood searching algorithm is introduced in the building phase. The simulated annealing algorithm is used at both the preservation of LNS (Large Neighborhood Search, LNS) results and each step of iterative search. The proposed method also presents a new iterative repair operator by combining the three operators of PDPTW, which has been proved to improve the search procedure effectively. Algorithm analyzing verifies the convergence of proposed method, moreover, a great deal of experimental results also show that the new hybrid algorithm maintains better global convergence.
(4) We propose a new algorithm named HIGA (Hierarchical Immune Genetic Algorithm) for Task First strategy. There exist complicated relevancies among the earth observing sub-tasks in the united scheduling, thus makes the united scheduling a constraints optimization problem. Based on the immune genetic algorithm, the dissertation proposes a new algorithm named HIGA which adopts the permutation based coding and a two-level coding. By introducing a hierarchical control mechanism, HIGA uses the genetic operator in the upper level and the immunity operator in the low level. Moreover, in the immunity operator, the algorithm adopts gene recombination, adaptive immunity updating and the niche mechanism to improve the performance of the searching process. Experimental results show that the algorithm and the improved operators is both feasible and effective in solving the problem of multiple satellites scheduling with complicated tasks being in scheduling.
(5) We propose the united scheduling process and algorithms for the task of moving target surveillance. Focusing on the task of moving target surveillance, the dissertation proposes a two-phase scheduling process including searching phase and surveillance phase. In the searching phase, in which condition there is no transcendental information, firstly, we uniformly partition the area using grid. Then we assume that the targets are randomly distributed in the searching area, and they are moving stochastically. Finally, we update the distribution probability of the moving target according to the historical observing results. Based on the above processes, a searching algorithm is proposed based on the dynamic updates of the distribution probability. In the surveillance phase, in which condition part of the transcendental information is observed, considering the effects of the geographical information and apriori information to the moving targets, an adaptive interactive multiple model algorithm for moving target surveillance is proposed, which supports the adaptive update and selection of the moving target movement model. Experimental results show that the proposed method is feasible and effective.
论文面向光学、SAR、电子侦察等多种类型的中低轨对地观测卫星联合任务规划的应用需求,以多类型对地观测卫星和多类型数传资源的联合为手段,以满足多种类型的观测任务请求为出发点,在总结分析国内外相关研究工作的基础上,对多类型对地观测卫星联合任务规划关键问题进行研究。主要工作和创新点包括:
(1)建立了多类型对地观测卫星联合任务规划层次化描述模型。由于多类型卫星联合任务规划问题的复杂性,论文系统地分析和总结了多类型卫星联合任务规划的规划要素,针对参与规划卫星载荷多样和观测任务请求多样的特点,将问题模型抽象为五个层次,并重点对资源约束、规划约束、业务组件、业务过程四个层次进行详细地建模描述;论文模型采用了组件式建模方法,约束和优化目标函数作为组件参与到模型中,在规划过程中可以根据观测任务请求特点、优化策略和规划要素约束特点动态地选择适宜的处理流程,每种流程又可以根据问题特点进行约束和优化目标函数的组合选择,从而使问题模型具有高度的可移植性和扩展性,能够较好地适应多类型卫星联合任务规划的复杂规划要求。
(2)提出了数传资源规划数学模型和优化算法。作为卫星任务规划的重要组成部分,数传资源规划对卫星动作规划具有重要影响,需要展开研究。论文以卫星观测过程中星载存储器变化为研究切入点,建立了基于约束图的数传资源规划模型,将数传资源规划问题转化为约束图中多顶点对之间的多条路径搜索问题;并基于置换序列表示方法,引入自适应邻域搜索机制以改进邻域搜索过程,提出了自适应邻域搜索模拟退火算法,算法收敛性分析和实验验证了算法的优化性。相对于其它方法,论文模型从星地联合的角度出发,考虑了观测任务分布、卫星载荷能力、卫星实传和回放动作对规划的影响,因而能够更贴切地满足对地观测卫星的数传需求,论文算法相对于模拟退火、随机爬山和遗传算法等算法也具有更好的全局收敛性。
(3)面向综合效益优先策略提出了基于贪婪随机自适应搜索过程算法框架的混合算法。面向综合效益优先策略的多类型对地观测卫星联合任务规划问题具有多时间窗约束车辆装卸和过载规划问题的问题特点。论文采用修复搜索问题求解机制,提出了一种新的混合算法,该算法基于贪婪随机自适应搜索过程求解框架,在构建阶段引入大邻域搜索算法,并在大邻域搜索结果保留和每一步的迭代搜索中分别应用模拟退火思想,算法同时将车辆装卸问题的三种邻域算子高效组合,提出一种新的迭代修复邻域搜索算子以改进算法搜索过程。算法收敛性分析验证了算法的全局收敛性,实验验证结果也表明算法具有更好的收敛性;该算法可对车辆装卸问题和过载规划问题提供一种新的求解思路。
(4)面向任务优先策略提出了分层控制免疫遗传算法。任务优先下多类型对地观测卫星联合任务规划的观测任务子任务之间具有复杂关联关系,使问题成为一个复杂的约束优化问题。论文基于免疫遗传算法框架,提出了分层控制免疫遗传算法;算法采用置换序列编码和双层编码方式,求解中引入分层控制机制,对顶层编码采用遗传操作算子,对底层编码采用免疫操作算子,并在免疫操作算子中引入免疫选择、免疫更新、小生境等思想以改进搜索过程,有效解决任务优先策略下的多类型卫星联合任务规划问题。实验结果表明该算法在组合观测任务参与规划的算例下是可行有效的。
(5)面向移动跟踪监视任务提出了多类型对地观测卫星联合任务规划流程和算法。针对移动跟踪监视任务,根据获取的先验信息的不同,提出了搜索发现+跟踪监视配合的任务规划流程:无先验信息条件下,即搜索发现阶段,对区域进行等距网格划分,假设目标位置在搜索区域内随机分布,目标运动模型为随机运动,提出了基于网格目标分布概率动态更新的搜索发现算法;部分先验信息已知条件下,即跟踪监视阶段,考虑地理信息和目标先验信息对目标运动的影响,借鉴交互多模型算法思想,引入目标运动模型的自适应更新和选择机制,提出自适应交互多模型移动目标跟踪监视算法,解决了面向移动跟踪监视任务的多类型卫星联合任务规划问题。实验验证表明上述流程和算法是可行有效的。
Earth Observing Satellites (EOSs) obtain information of the earth's surface from outer space by using satellite sensors. They are widely used in the social, economic, and military applications. In recent years, as more and more EOSs are launched to meet the needs of various complicated types of remote sensing data, the challenge to scheduling of multiple types of EOSs is increased. However, current researches focus on single type of satellite and simple requirements, which considers relatively simple earth observing procedure and restrictions, and lacks of methods and techniques for scheduling of multiple types of satellites, satisfying multiple types of requirements and combining satellite observing with data transmitting.
To satisfy the united scheduling requirements of multiple types of low orbit satellites, including optical satellites, SAR satellites, electronic satellites, and ocean surveillance satellites, this dissertation studies the key technologies of the united scheduling of multiple types of EOSs. Research focus is put onto combining multiple types of EOSs with multiple types of data transmitting resources, thus to meet the needs of various earth observing tasks. The main work and contributions of this dissertation can be concluded as the following five parts:
(1) We build a hierarchical description model for united scheduling of multiple types of EOSs. Due to the complexity of the united scheduling of multiple types of EOSs, the dissertation systematically analyzes and summaries the scheduling features in the target problem. Focusing on the characteristics of the scheduling workload and the diversity of the observing tasks, the dissertation concludes the problem model into five levels, and put focuses on four levels: resource constraints, scheduling constraints, business component and business process, meanwhile gives detailed description of the modeling process. The dissertation uses a component based modeling methodology to build the description model. Therefore, both the constraints and the optimization target are taken as components in the model. In the scheduling process, it is convenient to choose suitable process according to different characteristics of the observing tasks, the optimization strategy and the scheduling features. Moreover, during each process, the constraints and the optimization target functions can be combined dynamically according to different problem features. All of which make the description model highly compatible and extensible, thus could satisfy the complicated requirements of the united scheduling of multiple types of EOSs .
(2) We propose the techniques to the scheduling of various data transmitting resources from both the satellites and ground stations. Results of the scheduling of data transmitting play an important role in satellite scheduling; therefore it must be studied first. Considering the variation of the on-satellite memorizer during the earth observing, the dissertation establishes a scheduling model for data transmitting resources based on the constraint graph, which transforms the scheduling problem into a multiple paths searching problem. By using the permutation based representation, an adaptive neighborhood searching mechanism is introduced to improve the neighborhood searching process. Moreover, a simulated annealing algorithm based on the adaptive neighborhood searching is proposed. Analysis to the convergence of the algorithm and the experimental results verify that the proposed model and algorithm are superior. Compared to other algorithms, the proposed method synthetically considers the following factors: the distribution of the earth observing tasks, the capability of the satellites' load, the effects of the real time transmission and storage data transmission to the scheduling. Therefore, the method could satisfy the data transmitting requirements more suitably. Moreover, compared to the simulated annealing algorithm, the stochastic hill-climbing algorithm and the genetic algorithm, the proposed method maintains much better global convergence.
(3) We present a hybrid algorithm based on the GRASP (Greedy Randomized Adaptive Search Procedure, GRASP) framework for Benefit First strategy. The united scheduling problem has the similar characteristics with both the PDPMTW (Pickup and Delivery Problem with Multiple Time Windows) and oversubscribed scheduling problem. By using the mechanism of the repair search, the dissertation proposes a new hybrid algorithm which is based on the GRASP framework. The large neighborhood searching algorithm is introduced in the building phase. The simulated annealing algorithm is used at both the preservation of LNS (Large Neighborhood Search, LNS) results and each step of iterative search. The proposed method also presents a new iterative repair operator by combining the three operators of PDPTW, which has been proved to improve the search procedure effectively. Algorithm analyzing verifies the convergence of proposed method, moreover, a great deal of experimental results also show that the new hybrid algorithm maintains better global convergence.
(4) We propose a new algorithm named HIGA (Hierarchical Immune Genetic Algorithm) for Task First strategy. There exist complicated relevancies among the earth observing sub-tasks in the united scheduling, thus makes the united scheduling a constraints optimization problem. Based on the immune genetic algorithm, the dissertation proposes a new algorithm named HIGA which adopts the permutation based coding and a two-level coding. By introducing a hierarchical control mechanism, HIGA uses the genetic operator in the upper level and the immunity operator in the low level. Moreover, in the immunity operator, the algorithm adopts gene recombination, adaptive immunity updating and the niche mechanism to improve the performance of the searching process. Experimental results show that the algorithm and the improved operators is both feasible and effective in solving the problem of multiple satellites scheduling with complicated tasks being in scheduling.
(5) We propose the united scheduling process and algorithms for the task of moving target surveillance. Focusing on the task of moving target surveillance, the dissertation proposes a two-phase scheduling process including searching phase and surveillance phase. In the searching phase, in which condition there is no transcendental information, firstly, we uniformly partition the area using grid. Then we assume that the targets are randomly distributed in the searching area, and they are moving stochastically. Finally, we update the distribution probability of the moving target according to the historical observing results. Based on the above processes, a searching algorithm is proposed based on the dynamic updates of the distribution probability. In the surveillance phase, in which condition part of the transcendental information is observed, considering the effects of the geographical information and apriori information to the moving targets, an adaptive interactive multiple model algorithm for moving target surveillance is proposed, which supports the adaptive update and selection of the moving target movement model. Experimental results show that the proposed method is feasible and effective.
引文
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