Highlights Satellites optimally observe a priori unknown ground targets twice per day. Linear and quadratic constraints are developed to approximate the arccosine function. Sample Average Approximation is used due to number of scenarios.

    Abstract This study investigates a means to achieve twice-daily direct flyovers of a ground target whose location is unknown at the time of launch. The initial orbital parameters were chosen such that the expected cost of maneuvering to observe the ground target twice per day could be minimized. Previous work on this problem has only included one direct flyover daily and has left viewing a second pass during the day as a happenstance event. A two-stage stochastic programming model was developed and solved using the sample-average approximation (SAA) method along with the L-shaped method. As adding a second direct flyover daily results in a nonlinear model that includes an arccosine function, we proposed an approximation approach for the arccosine function as part of a quadratic program and evaluated the accuracy of our approach. Our results showed that for some scenarios, the maneuver costs were too high to allow for twice-daily direct flyovers, but in other scenarios, the cost was much more reasonable. However, by selecting initial orbital parameters that allowed for the potential of maneuvering for twice-daily direct flyovers, once the scenario had been realized, the mission manager could still decide whether the satellites should be maneuvered to achieve twice-daily observations.


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    Titel :

    Optimization of twice-daily direct flyover data collection for satellite observations at uncertain locations


    Beteiligte:
    Hoskins, Aaron B. (Autor:in) / Medal, Hugh R. (Autor:in) / Rashidi, Eghbal (Autor:in)

    Erschienen in:

    Advances in Space Research ; 70 , 4 ; 1013-1031


    Erscheinungsdatum :

    2022-05-31


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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