NSGA-II-Based Multi-objective Mission Planning Method for Satellite Formation System

Shao, Xiaowei; Zhang, Zehao; Wang, Jihe; Zhang, Dexin

doi:10.5028/jatm.v8i4.700

Cited by 6 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…The proposed approach was evaluated with some previous work such as a mixed-integer linear program MILP [18], NSGA-II [19], and simulated annealing [20]. Some of these clustering methods have been used for a set of targets with common features.…”

Section: Computational Results and Discussionmentioning

confidence: 99%

Multi-objective optimization for multi-satellite scheduling task

Khojah

Mosa²

2022

JOSCEX

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The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is superior to its peers from other methods.

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Section: Computational Results and Discussionmentioning

confidence: 99%

Multi-objective optimization for multi-satellite scheduling task

Khojah

Mosa²

2022

JOSCEX

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“…Typical regional decomposition methods include the grid method and the strip method. The grid method [40] divides the regional target into a collection of point targets, and the strip method [18,[28][29][30][31]36,37] decomposes the regional target into a rectangular strip according to certain rules that can be completely covered by a single transit of the satellite. Hu [25] proposed a grid-based split method that generates more flexible strips than the traditional parallel split method, and proved that the optimal solution can be achieved on all basic covering strips [27].…”

Section: Single Satellite Multi-satellitesmentioning

confidence: 99%

Multi-Satellite Imaging Task Planning for Large Regional Coverage: A Heuristic Algorithm Based on Triple Grids Method

Li,

Wan,

Wen

et al. 2024

Remote Sensing

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Over the past few decades, there has been a significant increase in the number of Earth observation satellites, and the area of ground targets requiring observation has also been expanding. To effectively utilize the capabilities of these satellites and capture larger areas of ground targets, it has become essential to plan imaging tasks for large regional coverage using multiple satellites. First, we establish a 0-1 integer programming model to accurately describe the problem and analyze the challenges associated with solving the model. Second, we propose a heuristic algorithm based on the triple grids method. This approach utilizes a generated grid to create fewer candidate strips, a calculation grid to determine the effective coverage area more accurately, and a refined grid to solve the issue of repeated coverage of strips. Furthermore, we employ an approximation algorithm to further improve the solutions obtained from the heuristic algorithm. By comparing the proposed method to the traditional greedy heuristic algorithm and three evolutionary algorithms, the results show that our method has better performance in terms of coverage and efficiency.

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“…On this basis, a mathematical model for the reconnaissance satellite scheduling problem was established, which optimizes the two objective functions of the maximum observation benefit and minimum observation cost in a hierarchical priority manner. Shao [18] decomposed regional targets into multiple point targets using a grid of equal latitude and longitude and established a planning model with the maximum total revenue and maximum number of completed tasks as objective functions. The digital elevation model and moving target on the ground are obtained simultaneously using the satellite formation flight system.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

Chen

Shen

et al. 2020

Remote Sensing

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Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly.

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NSGA-II-Based Multi-objective Mission Planning Method for Satellite Formation System

Cited by 6 publications

References 7 publications

Multi-objective optimization for multi-satellite scheduling task

Multi-objective optimization for multi-satellite scheduling task

Multi-Satellite Imaging Task Planning for Large Regional Coverage: A Heuristic Algorithm Based on Triple Grids Method

A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

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