Abstract
This study proposes a non-dominated sorting genetic algorithm-II-based multi-objective optimization method to solve the multi-objective mission planning problem for satellite formation flying system which has the ability to obtain both digital elevation map and ground moving target indicator information at the same time when certain conditions are satisfied. The 2 objectives considered in this study are maximizing total profits and maximizing numbers of completed acquisitions. Thus, the multiple-objective satellite scheduling optimization problem is formulated and solved by the proposed method. Its validity and effectiveness are verified by numerical simulations, and the results show that it can achieve better performance with respect to 2 different objectives in an overall perspective than the traditional scheduling optimization, which can consider only 1 objective
Highlights
With the development of small satellite technology, formation flying becomes a new enabling technique for many space missions, such as virtual synthetic aperture radar (SAR), space surveillance system, etc
We focus on the interferometric SAR (InSAR) mission by using new proposed formation flying technology
To increase the system effectiveness, it is essential to propose a multiple-objective satellite scheduling optimization method to deal with the new formation flyingbased InSAR mission planning problem
Summary
With the development of small satellite technology, formation flying becomes a new enabling technique for many space missions, such as virtual synthetic aperture radar (SAR), space surveillance system, etc. To increase the system effectiveness, it is essential to propose a multiple-objective satellite scheduling optimization method to deal with the new formation flyingbased InSAR mission planning problem. The contribution of this study is that, to solve the unique formation flying SAR mission planning which can perform 2 different tasks at the same time, a non-dominated sorting genetic algorithm (NSGA)-II-based multi-objective mission planning optimization method with several practical constraints is proposed. We assume that the number of strips for each orbit is equal, and the satellite can perform 2 imaging tasks (DEM and GMTI) by using the first 3 strips (1, 2, and 3); T = {t1, t2, L, tm} is the set of observation targets including point and regional targets, where denotes the total number of targets.
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