Satellite Orbit Optimization

One of my former students, Aaron Hoskins, and I have done some work on using optimization to improve the ability of satellites to monitor an event (e.g, a wildfire). We developed several different stochastic programming models for locating ground stations that download data from satellites (Hoskins and Medal, 2019) and optimizing the orbital parameters of a constellation of satellites (Hoskins and Medal, 2017). Extending the work of Hoskins and Medal (2017), which only considered data collection during the ascending pass of a satellite’s orbit, we also developed a model for collecting data during both the ascending and descending passes (Hoskins and Medal, 2020)

1. Optimization of twice-daily direct flyover data collection for satellite observations at uncertain locations
   AB Hoskins, HR Medal, E Rashidi. AB Hoskins, HR Medal, E Rashidi (2022) Advances in Space Research 70 (4), 1013-1031
   Publication Year: 2022
   [Link to Article]

2. Stochastic Programming Solution for Placement of Satellite Ground Stations
   Aaron Hoskins, Hugh Medal. To appear in Annals of Operations Research.
   Publication Year: 2019
   Abstract
   Disaster recovery efforts are enhanced through the collection and dissemination of satellite data, which is downloaded from satellites to ground stations. The optimal ground station locations vary depending on the location of the disaster, but ground station construction occurs before the realization of a disaster. Thus, a stochastic optimization problem arises: decide the location of ground stations before disasters with uncertain locations. We use a stochastic programming approach to select the location of ground stations given a set of potential disaster scenarios. The objective is to maximize the expected amount of data downloaded. The problem formulation consists of a two-stage stochastic program where the first-stage determines the locations of the ground stations and the second-stage schedules the uploading and downloading of data. We solve the problem using the L-shaped method; we find that it significantly outperforms solving the deterministic equivalent problem directly. We also find that an alternate second-stage formulation significantly improves solution time. The optimized set of ground stations found by our algorithm is compared to the set of ground stations operated by the National Oceanic and Atmospheric Administration’s; results confirm that the current placement is effective and demonstrate the benefit in adding additional ground stations.

3. Satellite Constellation Design for Forest Fire Monitoring Via a Stochastic Programming Approach
   Aaron Hoskins, Hugh Medal, Eghbal Rashidi. To appear in Naval Research Logistics.
   Publication Year: 2017
   Abstract
   There is significant value in the data collected by satellites during and after a natural disaster. The current operating paradigm in practice is for satellites to passively collect data when they happen to fly over a disaster location. Conversely, this article considers the alternative approach of actively maneuvering satellites to fly directly overhead of the disaster site on a routine basis. Toward this end, we seek to compute a satellite constellation design that minimizes the expected maneuver costs for monitoring an unknown forest fire. In this article, we present a 2‐stage stochastic programing model for this problem as well as a accelerated L‐shaped decomposition approach. A comparison between our approach and the current operating paradigm indicates that our solution provides longer duration data collections and a greater number of data collections. Analysis also shows that our proposed solution is robust over a wide array of scenarios.