Satellite Swarm Localization and Control via Random Finite Set Statistics

The proposed novel program will develop and demonstrate a new approach to perform real-time relative vehicle localization within a swarm formation with application to communication-less coordination. These objectives are achieved by using Random Finite Sets statistics theory to solve the multiple object tracking problem. The swarm formation localization problem can be formulated as estimating the local intensity function of targets in the near field and developing probabilistic control strategies to track an expected localization state space configuration. Work will focus on developing estimation and control algorithms that can utilize simple measurements range and bearing angle to other units, and also determine the local environment using feature measurements. Three major tasks are proposed for the development of swarming space vehicle estimation and control: Random Finite Set Localization, Random Finite Set Formation Control, and Bayesian Collective Decision Making. Algorithm development in Phase I will extend to a Hypothesis Density Filter and Sequential Monte Carlo Hypothesis Density Filter, Motion Model, Estimation techniques, Landmark SLAM using these techniques, Behavioral Distribution Control, Cyclic Distribution Control, and multiple decision making estimation models. Proposed follow-on efforts will fully implement the swarm technology for real-time integrated system use, identify different formation configurations and sensor combination for hardware integration, and work to position the system for integration into a demonstration mission identified in the Phase I work to fully illustrate the mission enhancements of the operational system.