Research On Method Of Optimization Of Top Design Parameters For Earth Observation Satellite System

As main ground information collecting platforms in space, earth observation satellite systems (EOSS) are charactered by broad coverage, high security performance and unlimited to airspace and national boundaries. As a result, they play outstanding roles in both military demands and civil applications, owning extrusive strategic significance and importance. In the past decades, earth observation satellites in China have been developed greatly, which are incarnated on not only the number but also quality of the satellites. However, the holistic system performance is not high; especially the capabilities of fast response and coverage for important area are bad. Therefore, we propose problem of top design parameters optimization for EOSS.Through configuring system key parameters in a reason way, we can improve performance of EOSS. Based on the research background mentioned above, the main work and innovative points in this paper are as follows.Firstly, bring forward the problem of top design parameters optimization for EOSS, introducing the necessaries of our research from the view of engineering. Also we analyze the complexity of the problem from both physical structure and its performance computation. Grounded on the analysis, a simulation based optimization method is put forward. More precisely, a framework which incorporated design of experiment and surrogate models is constructed.EOSS is designed to image particular targets to fulfill different user requests. Hence, performances of EOSS are mainly defined as coverage performance. By adopting the method of point based numeral simulation we can research performance of EOSS. And a series of measures, which can be divided into two groups: space and time, are proposed to describe the capability of EOSS. Also, we figured out system variables that might affect proposed measures. And many simulation tests are carried out to validate effects of different variables.According to the above discuss, to optimize top design parameters of EOSS we need simulation to calculate its performance. To create simulation plans with fewer test points, we proposed comprehensive latin hyper-cube (CLHD) method. The plans generated by CLHD ensure its sampling points are uniformly distributed in the design space and correlations between points are small. To realize CLHD, we first analyze optimal criterions for Latin hypercube array and define a multi-objective benefit function. The main body of CLHD is carried out by very fast simulated annealing (VFSA) algorithm, in which four transforming neighborhoods are defined and its annealing schedule follows a Cauchy function. Arrays with good orthogonality, which are constructed by Cholesky decomposition, are used as initial solution for VFSA. Finally, many instances are introduced to testify the effectiveness of CLHD. We also analyze the influence of different system variables.EOSS simulation will produce abundant simulation data. And a multi-point updated Kriging surrogate model is constructed to simulate and approximate these data. Points with optimized value or maximal expected improvement are selected to update our surrogate model. And a measure named objective improvement versus distance is defined to filter the selected points. To get optimized solution of the surrogate model, we construct improved generalized pattern search algorithm. In search step, genetic algorithm and sequential quadratic programming are used to find potential update points. In poll step, dynamic incompletion poll is carried out to find points with greater value. And several benchmark functions are used to test the proposed method and the results show that our method own an outstanding global search ability.Finally, we address the simulation structure, flow of EOSS and detail the scenario design and process of system optimization. Then two application examples are designed. One is back grounded on the fast launch of micro-satellite, which is to maximize the coverage percentage of target area. The other is for global coverage and its objective is to minimize revisit time of EOSS. These two instances are used to illustrate the flow of our method. They also testify the validity of the method we put forward. Comparing to the results got by Analyzer, our method is more effective.