<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The ongoing enhancement of imaging satellite platforms in terms of payload capacity, coupled with the proliferation of imaging satellites, introduces new complexities to the mission planning processes. These enhancements enable broader applications and significantly increase the societal benefits derived from imaging satellites. To address these challenges, a specific kinematic model for dynamic imaging attitudes is constructed, taking into account the dynamics of satellite imaging missions. This model uses information from satellite imaging observation tasks to design constraints that govern the planning of imaging tasks. Additionally, an optimization objective function is established to ensure compliance with these planning constraints. Building on the encoding method for relative imaging moments, an adaptive genetic algorithm tailored for satellite imaging task planning is introduced. This algorithm enhances the iterative efficiency of decision variables involved in satellite imaging tasks. Empirical validation through comparative simulation experiments, using a typical satellite imaging mission as a case study, demonstrates the effectiveness of the adaptive genetic algorithm. In various phases of imaging mission planning, the algorithm achieved a 100% task completion rate. The index function gain was enhanced by 21.47%, and the maximum synthetic angular velocity of attitude maneuvers between different targets peaked at the satellite’s maneuvering threshold of 7 degrees per second. By leveraging adaptive genetic algorithms, satellite imaging mission planning can optimize mission completion rates and effectively utilize the satellite’s maximum attitude maneuver capabilities.</p>
</abstract>

The ongoing enhancement of imaging satellite platforms in terms of payload capacity, coupled with the proliferation of imaging satellites, introduces new complexities to the mission planning processes. These enhancements enable broader applications and significantly increase the societal benefits derived from imaging satellites. To address these challenges, a specific kinematic model for dynamic imaging attitudes is constructed, taking into account the dynamics of satellite imaging missions. This model uses information from satellite imaging observation tasks to design constraints that govern the planning of imaging tasks. Additionally, an optimization objective function is established to ensure compliance with these planning constraints. Building on the encoding method for relative imaging moments, an adaptive genetic algorithm tailored for satellite imaging task planning is introduced. This algorithm enhances the iterative efficiency of decision variables involved in satellite imaging tasks. Empirical validation through comparative simulation experiments, using a typical satellite imaging mission as a case study, demonstrates the effectiveness of the adaptive genetic algorithm. In various phases of imaging mission planning, the algorithm achieved a 100% task completion rate. The index function gain was enhanced by 21.47%, and the maximum synthetic angular velocity of attitude maneuvers between different targets peaked at the satellite’s maneuvering threshold of 7 degrees per second. By leveraging adaptive genetic algorithms, satellite imaging mission planning can optimize mission completion rates and effectively utilize the satellite’s maximum attitude maneuver capabilities.

A dynamic planning method for satellite imaging mission based on improved genetic algorithm

Science and Technology on Complex Electronic System Simulation Laboratory

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The ongoing enhancement of imaging satellite platforms in terms of payload capacity, coupled with the proliferation of imaging satellites, introduces new...

A dynamic planning method for satellite imaging mission based on improved genetic algorithm

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 17. Feb. 2024

Akzeptiert: 14. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1526

Schlüsselwörter
Satellite imaging in motion, Imaging attitude, Imaging mission planning, Relative imaging moment, Adaptive genetic algorithm

© 2024 Demin Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A dynamic planning method for satellite imaging mission based on improved genetic algorithm

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 17. Feb. 2024

Akzeptiert: 14. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1526

SchlüsselwörterSatellite imaging in motion, Imaging attitude, Imaging mission planning, Relative imaging moment, Adaptive genetic algorithm

© 2024 Demin Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Satellite imaging in motion, Imaging attitude, Imaging mission planning, Relative imaging moment, Adaptive genetic algorithm