卫星对地观测任务规划问题简明综述 *

卫星对地观测任务规划是为了满足用户的遥感图像需求 ,对遥感卫星系统资源和对地观测任务进行规划与调度的过程。合理的任务规划是提高遥感卫星系统效能的重要手段。为此 ,分析了卫星对地观测任务规划问题的主要特点 ,比较了这一问题的若干常用建模方法和求解技术 ,并探讨了卫星对地观测任务规划技术的未来发展趋势。     

一种面向任务的对地观测卫星Agent团队构建方法

随着航天科技的飞速发展,逐渐出现了由多种异构卫星组成的卫星集群。相比于传统的卫星系统,卫星集群具有规模大、平台多、载荷异构的特点,传统的卫星任务规划方法难以适用。针对卫星集群任务规划中的关键问题——面向任务的卫星Agent团队构建问题,建立了数学模型,提出了基于分支限界的精确搜索算法,并对其时间复杂度进行了分析。针对精确算法时间复杂度较高的缺点,引入了启发式剪枝机制,并按照任务集合排序策略的不同设计了3种启发式卫星团队构建算法。最后,通过多组实验分析了卫星团队构建精确搜索算法与启发式剪枝搜索算法的性能,验证了我们提出算法的有效性和实用性。     

Exact algorithms for single frame selection on multiaxis Satellites

New multi-axis satellites allow camera imaging parameters to be set during each time slot based on competing demand for images, specified as rectangular requested viewing zones over the camera's reachable field of view. The single frame selection (SFS) problem is to find the camera frame parameters that maximize reward during each time window. We formalize the SFS problem based on a new reward metric that takes into account area coverage and image resolution. For a set of n client requests and a satellite with m discrete resolution levels, we give an algorithm that solves the SFS problem in time O(n/sup 2/m). For satellites with continuously variable resolution (m=/spl infin/), we give an algorithm that runs in time O(n/sup 3/). We have implemented all algorithms and verify performance using random inputs. Note to Practitioners-This paper is motivated by recent innovations in earth imaging by commercial satellites. In contrast to previous methods that required waits of up to 21 days for desired earth- satellite alignment, new satellites have onboard pan-tilt-zoom cameras that can be remotely directed to provide near real-time response to requests for images of specific areas on the earth's surface. We consider the problem of resolving competing requests for images: Given client demand as a set of rectangles on the earth surface, compute camera settings that optimize the tradeoff between pan, tilt, and zoom parameters to maximize camera revenue during each time slot. We define a new quality metric and algorithms for solving the problem for the cases of discrete and continuous zoom values. These results are a step toward multiple frame selection which will be addressed in future research. The metric and algorithms presented in this paper may also be applied to collaborative teleoperation of ground-based robot cameras for inspection and videoconferencing and for scheduling astronomic telescopes.     

基于强化学习的卫星网络资源调度机制

与传统同步轨道通信卫星(GEO)相比,以SpaceX、Starlink、O3b等为代表的新一代中低轨卫星互联网星座具备广域覆盖、全时空互联、多星协同等显著优势,已成为当今世界各国研究的焦点之一。传统卫星资源调度方法主要研究单颗GEO卫星下的资源调度问题,难以满足以多星协同、联合组网、海量用户为特征的低轨卫星星座的资源调度需求。为此,构建了基于用户满意度的多星协同智能资源调度模型,提出了一种基于强化学习的卫星网络资源调度机制IRSUP。IRSUP针对用户服务定制的个性化需求,设计了用户服务偏好智能优化模块;针对多星资源联合优化难题,设计了基于强化学习的智能调度模块。模拟仿真结果表明:IRSUP能有效提高资源调度合理性、链路资源利用率和用户满意度等指标,其中业务容量提升30%～60%,用户满意度提升一倍以上。     

中继卫星与用户航天器之间星间链路的研究

中继卫星在地球同步静止轨道上运行,既能直视中低轨道用户航天器,又能直视地面站,是沟通用户航天器与地面站的桥梁。如果考虑中继卫星星座,则中低轨道用户航天器基本上在中继卫星星座的覆盖范围内,因此只考虑单颗中继卫星与用户航天器之间的星间链路情形。中继卫星与中低轨道用户航天器之间的星间链路可以用中继卫星与用户航天器之间的时间窗口个数、平均时间窗口长度、最大和最小的时间窗口长度等参数描述。利用STK,采用模拟仿真的方法,分析了中继卫星与用户航天器之间的星间链路特性。     

Multidisciplinary modeling and surrogate assisted optimization for satellite constellation systems

Satellite constellation system design is a challenging and complicated multidisciplinary design optimization (MDO) problem involving a number of computation-intensive multidisciplinary analysis models. In this paper, the MDO problem of a constellation system consisting of small observation satellites is investigated to simultaneously achieve the preliminary design of constellation configuration and the satellite subsystems. The constellation is established based on Walker-δ configuration considering the coverage performance. Coupled with the constellation configuration, several disciplines including payload, power, thermal control, and structure are taken into account for satellite subsystems design subject to various constraints (i.e., ground resolution, power usage, natural frequencies, etc.). Considering the mixed-integer and time-consuming behavior of satellite constellation system MDO problem, a novel sequential radial basis function (RBF) method using the support vector machine (SVM) for discrete-continuous mixed variables notated as SRBF-SVM-DC is proposed. In this method, a discrete-continuous variable sampling method is utilized to handle the discrete variables, i.e., the number of orbit planes and number of satellites, in the satellite constellation system MDO problem. RBF surrogates are constructed and gradually refined to represent the time-consuming simulations during optimization, which can efficiently lead the search to the optimum. Finally, the proposed SRBF-SVM-DC utilized to solve the satellite constellation system MDO problem is compared with a conventional integer coding based genetic algorithm (ICGA). The results show that SRBF-SVM-DC significantly decreases the system mass by about 28.63% subject to all the constraints, which greatly reduces the cost of the satellite constellation system. Moreover, the computational budget of SRBF-SVM-DC is saved by over 85% compared with ICGA, which demonstrates the effectiveness and practicality of the proposed surrogate assisted optimization approach for satellite constellation system design.     

A Constraint Satisfaction Neural Network and Heuristic Combined Approach for Concurrent Activities Scheduling

Scheduling activities in concurrent product development process is of great significance to shorten development lead time and minimize the cost. Moreover, it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible. This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling. In the combined approach, the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints, the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constraints. The feasible scheduling solution is obtained by a gradient optimization function. Simulations have shown that the proposed combined approach is efficient and feasible with respect to concurrent activities scheduling.     

A constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling

Scheduling activities in concurrent product development process is of great sig-nificance to shorten developements lead time and minimize the cost.Moreover,it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible,This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling.In the combined approack,the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints ,the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constrainsts.The feasible scheduling solution is obtained by a gradient optimization function .Sim-ulations have shown that the proposed combined approach is efficient and fasible with respect to concurrent activities scheduling.     

基于Petri网和混合蚁群算法的多星成像调度

提出一种基于综合指标Petri网和混合蚁群算法的多星成像调度策略。在综合指标Petri网变迁中引入指标信息,处理多星并发观测和卫星资源竞争关系、反映卫星能量和存储等约束,使得问题描述更直观和完备。设计一种嵌入局部搜索技术的蚁群优化算法,通过启发式信息综合变迁中的指标,引导蚂蚁进行全局搜索。仿真实例结果表明,该策略能有效求解多星成像调度问题,实现全局搜索和快速收敛的平衡。     

多观测卫星任务调度系统的设计与实现

随着卫星应用技术的飞速发展,对地观测卫星在减灾防灾、环境保护和军事等领域的应用也更加广泛.该文在卫星仿真工具包(STK)的基础上设计并实现了多观测卫星任务调度系统,描述了系统的结构和功能,重点介绍了任务描述、调度引擎等重要模块的设计方法和实现过程,并用甘特图等方式实现了系统的结果展现,取得了良好的效果,最后对系统的特点和发展方向做出了总结和展望.     

面向异构通信卫星的地球站组网规划方法

经过多年的建设发展,卫星通信已经形成了多系列卫星并存、相互支撑、相互补充的通信系统。然而,由于各卫星系统建设时间跨度大,通信技术手段存在差异,如何高效利用异构的卫星通信资源成为了一个实际难题。为此,文中首次根据卫星与地球站的可互通条件构建了系统模型,并将其归结为线性约束下离散变量的非线性优化问题,优化目标是异构卫星波束间的负载均衡,并在此基础上设计了一种两阶段的启发式组网规划方法。最后,根据地球同步卫星网络特性设置了合理的仿真参数,利用Matlab平台进行仿真实验。仿真结果表明,所提方法在面对不同规模的地球站组网时,在平均负载和负载均衡性上均优于一般的枚举搜索和遗传优化算法,可以在短时间内对大规模的信道单元进行组网规划。     

Evolutionary algorithms + domain knowledge = real-world evolutionary computation

We discuss implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs). We also describe offline and online metaheuristics as examples of explicit methods to leverage this knowledge. We illustrate the benefits of this approach with four real-world applications. The first application is automated insurance underwriting-a discrete classification problem, which requires a careful tradeoff between the percentage of insurance applications handled by the classifier and its classification accuracy. The second application is flexible design and manufacturing-a combinatorial assignment problem, where we optimize design and manufacturing assignments with respect to time and cost of design and manufacturing for a given product. Both problems use metaheuristics as a way to encode domain knowledge. In the first application, the EA is used at the metalevel, while in the second application, the EA is the object-level problem solver. In both cases, the EAs use a single-valued fitness function that represents the required tradeoffs. The third application is a lamp spectrum optimization that is formulated as a multiobjective optimization problem. Using domain customized mutation operators, we obtain a well-sampled Pareto front showing all the nondominated solutions. The fourth application describes a scheduling problem for the maintenance tasks of a constellation of 25 low earth orbit satellites. The domain knowledge in this application is embedded in the design of a structured chromosome, a collection of time-value transformations to reflect static constraints, and a time-dependent penalty function to prevent schedule collisions.     

基于启发式遗传算法的时分星间网络拓扑优化设计

针对北斗导航系统对星间网络的测量与数传业务需求,提出一种基于启发式遗传算法的网络拓扑优化设计方法,首先通过理论分析制定了拓扑规划的基本原则,给出一个能满足大部分需求的拓扑框架,以此作为初值,利用遗传算法对拓扑进行了优化,在路由规划中采用了分时以最短时延为原则的路由算法,最后,对星间网络测量指标进行了统计分析,以星间数据传输和星星地数据下传作为星间网络数据传输的典型工况,对星间网络拓扑路由规划的结果进行了逻辑仿真验证,仿真结果表明,该网络拓扑规划能满足导航星座常规业务的基本测量与数传需求,为解决导航星座星间网络的复杂运行管理提供了一种解决问题的思路。     

考虑任务依赖的卫星边缘计算资源分配与卸载决策算法

针对高低轨卫星网络协同边缘计算的卸载决策问题,提出了一种考虑任务依赖的联合计算资源、无线资源分配与任务调度的卫星网络边缘计算卸载决策算法。首先,将任务卸载问题建模为最小化任务延迟和能量消耗的联合优化问题;然后,将能源消耗和时延引入子任务优先级定义中,基于动态优先级进行启发式卸载策略搜索。该算法保证了子任务之间的依赖性并同时考虑了无线资源分配。仿真结果表明,与已有研究相比,该算法能缩短高低轨卫星协同计算的任务执行延迟,且能够降低低轨卫星功耗。     

An efficient utilization of intermittent surface–satellite optical links by using mass storage device embedded in satellites

Recently, earth observation system by using satellite network has attracted much attention due to its wide coverage and disaster resistance. Although the system is useful for collecting various data, which have an effect on a natural disaster, ecology and so forth, earth observation satellite hardly send the collected observation data to the ground station. This is because that the earth observation satellite needs to orbit near surface of the earth to get high-precision data, and it limited the time that can be used to send the observed data traffic to the ground station. Additionally, the amount of the observed data drastically increase in these days. Thus, we focus on the data relay satellite using optical communication in this network. By relaying observed data to traffic to the relay satellite, which has geostationary orbit, it is possible to increase the chance of sending data for the observation satellite due to the wide coverage of the relay satellite. In addition, laser light that is used in optical communication in satellite network has high frequency and it can deliver large data compared with radio wave. However, laser light is greatly influenced by atmosphere, and optical link capacity between satellite and ground station drastically changes according to weather condition. Therefore, we propose a new data traffic control method to use the network constructed by satellites which has mass storage device effectively according to the condition of optical downlink between satellite and optical ground station. The effectiveness of the proposed method is evaluated with numerical result.     

星座卫星在轨长期管理测控策略研究

近年来,低轨大型卫星星座蓬勃发展,使得在轨卫星数量急剧增加,卫星在轨长期管理任务面临巨大挑战。首先分析了卫星星座在轨长期管理面临的挑战,针对星座卫星在轨长期管理面临的几种常见应用场景,通过理论分析扩频体制测控应答机的抗干扰机理,结合地面概念星卫星平台模拟验证,在微波暗室通过地面概念星卫星平台对星座卫星在轨长期管理肯能出现的情况进行模拟。模拟不同星座卫星在轨运行场景以及相应的测控应对策略,验证了多星同时入境时的卫星长期管理策略的可行性,给出星座卫星在轨长期管理的有效应对策略。为卫星在轨长期管理以及后续可能遇到的星座在轨长管问题提供参考。     

基于禁忌搜索算法测地卫星任务调度研究

研究测地卫星调度问题,它是一个复杂的组合优化问题,涉及多个卫星,大量的任务与约束限制,何时和如何执行每项任务。分析了问题的主要约束,在合理的假设基础上建立了带有时间窗口多资源调度问题模型,提出一种改进的禁忌搜索算法求解该模型。用实例对模型和算法进行了验证,并将结果与动态规划方法求解结果比较分析,结果表明模型和算法是有效的。     

合成孔径雷达数据减灾应用潜力研究综述

合成孔径雷达SAR(Synthetic Aperture Radar)技术是一种主动的对地观测技术,已在国内外减灾领域得到了广泛的应用。环境减灾HJ-1C SAR卫星是我国第一颗民用SAR卫星,也是我国第一颗用于减灾救灾领域的SAR卫星,已于2012年11月19日发射成功。在HJ\|1C SAR卫星发射前,从SAR技术特点出发,分别分析了单极化、多极化、干涉、极化干涉SAR数据在灾害预警监测评估领域的应用现状及潜力。对于推动利用SAR技术进行灾害监测预警业务体系向更深层面发展,推动高级空间技术在减灾领域的应用,以及推动我国在灾害监测预警领域的SAR卫星星座的建设具有重要意义。     

卫星任务调度统一化建模与多策略协同求解方法

针对卫星任务调度“一星一系统”、测运控分离的现状,以卫星执行任务产生的星上独立事件和星地协同事件作为调度对象,以事件可执行时机作为调度资源,建立卫星任务调度统一化约束满足模型,将传统运控任务调度与测控任务调度纳入统一的建模方法.为保障模型的通用性和适应性,设计包含构造启发式、智能优化和针对性算法改进的多策略协同求解方法,搭建卫星任务调度算法与调度模型松耦合、模块化的系统架构.实验测试表明,所提出方法能够弥补传统模型在敏捷遥感卫星任务调度和高轨卫星测控调度场景下的局限性,在Benchmark问题和实际应用场景中均表现出良好的适用性和优化效果.     

适应于组网卫星的数字化及自动化测试技术研究

随着各类卫星星座计划的提出和部署,航天卫星数量的逐步增加,传统的卫星研制模式已逐渐不满足卫星快速发射需求,为缩短卫星研制周期,卫星批量化、自动化测试手段逐渐成为新形势下卫星研制的关键一环。面向当前微小卫星高密度快速组网应用的需求,结合批产化卫星技术特点,研究了一种适用于组网卫星的数字化和自动化测试技术。对卫星综合测试系统架构进行优化,可满足多星批量化及并行测试;在卫星综合测试系统中增加自动化测试功能,可通过在线配置自动化序列实现卫星自动化测试;研发了一种数字卫星仿真测试平台,可用于卫星单机产品齐套前整星功能开发、测试等。克服了传统卫星研制周期长、测试过程数据判读和数据处理难度大以及重复性工作较多的缺陷。