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Multi-objectives optimization of earth observation micro-satellite design using particle swarm
Triharjanto R.H.a, Poetro R.E.a, Hardhienata S.b
a Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Indonesia
b Indonesian National Institute of Aeronautics and Space, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2014 IEEE.The objective of the research is to computationally get the best satellite system design, according to the performance criteria defined. The satellite type used in this research is the one that weight no more than 100 kg and dimension can be fitted into auxilary payload allocation in the Low-Earth-Orbit (LEO) launch vehicle. The satellite mission chosen in this research is Earth observation using 2 optical payloads of 4-band multispectral imager. The 2 performance criteria chosen are the image resolution and the image swath, which are trade-off of one another. The constrains on the design, in addition to the satellite launch envelope, is the downlink data rate. The aspects of subsystem design are assumed to be based on available technologies. The optimization is done using multi-objective particle swarm algorithm (MOPSO) scheme. The result obtained is the pareto front presenting choices for the designer on the satellite with optimal image resolution and swath, which then validated by comparing with manual calculation. The recomended parameter combinations are then used to calculate satellite system parameters.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Low earth orbit(LEO),Micro satellite,Multi-objectives optimization,Parameter combination,Particle swarm,Particle swarm algorithm,Performance criterion,Satellite system design[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]micro-satellite design,multi-objective optimization,particle swarm[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICARES.2014.7024371[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]