On The Integrated Scheduling Techniques For Imaging Satellites

Imaging from space is an important way to obtain the information about the earth's surface. Nowadays, the imaging applications in the field of social economy and military operation are featured by: broadly distributed imaging areas, frequent observing, various kinds of imaging satellites, instant obtaining of the imaging data, all of which lead to the rapid proliferation of earth observing demands. With the development of earth observing system composed of various imaging satellites, ground stations and data relay satellites, the control center as the core of the earth observing system needs to globally assign tasks to different imaging satellites and the corresponding receiving networks in order to image and receive image data, so that the earth observing needs can be maximally met. However, most of the current researches on earth observing scheduling are classified into two independent categories, i.e., satellites imaging scheduling and imaging data receiving scheduling. Thus there lacks a holistic method to integrate the two stages.This dissertation proposes the integrated scheduling techniques for imaging satellites to holistically optimize both of the imaging process and data receiving process. In the dissertation, the integrated scheduling model is taken as the research foundation; scheduling optimization technologies through solution space transferring are taken as the research focus; moreover, relaxation methods are studied to validate the effectiveness of the proposed methods. The main work and contributions of this dissertation can be concluded as the following four parts:1. Building the integrated scheduling model that comprises various kinds of resources.The integrated scheduling model is the foundation of the whole research. Upon the thorough analysis on imaging satellites and the receiving network, the factors and their relations that should be considered in modeling the problem are summarized. An integrated scheduling model that comprises both the imaging process and receiving process is presented. Meanwhile, the proposed model is adaptive to changes of scheduling period.2. Putting forward solution transfer method by permutation based representation for the integrated scheduling.The integrated scheduling problem, which is non-linear and complicated, belongs to constraints optimization problems. Therefore, it is difficult to construct and optimize the feasible schedules through directly searching in the space of schedules. Thus, a solution space transfer method by permutation based representations is proposed. Through assigning resources to the permutations, the method transfer the searching of feasible scheduling from the space of schedules to the non-constraint space of permutations.3. Presenting two kinds of optimization algorithms in the space of permutations to obtain optimal schedules.Optimal schedules can be obtained by searching in the space of permutations for individuals with maximum fitness value. Characteristics of the swap neighborhood and insertion neighborhood in the space of permutations are analyzed based on neighborhood graph and symmetric group theories. Then the dissertation puts forward a stochastic neighborhood search algorithm with memory, which has simple structure and converges very fast. To obtain more optimized schedules, a hybrid genetic search algorithm is proposed. The algorithm can keep its global astringency; what's more, it strengthens its local optimizing ability through neighborhood searching.4. Presenting Lagrange relaxation method to estimate the optimized integrated scheduling.The Integrated scheduling problem belongs to NP-Hard combinatorial optimization problems. The search algorithms can only approach the optimize schedules at limited computing costs. Thus, methods are needed to evaluate the optimal schedules. The dissertation proposes Lagrange relaxation method to solve this problem. A longest path search algorithm with polynomial complexity and a subgradient optimization algorithm are presented to solve the relaxation problem; therefore, the tight upper bound for the original problem can be gained. The experimental results show that schedule from the search algorithms in the space of permutations is very close to the upper bound, which proves that our proposed optimization algorithms are effective and can meet the needs of optimizing the integrated scheduling.Based on the above achievements, the thesis designs and implements an experimental system, which has been used to solve a worldwide earth observation proble through multiple imaging satellites and receiving groundstations. This system validates the efficiency and practicability of the presented techiques.