Autonomous Multi-Phenomenology Space Domain Sensor Tasking and Adaptive Estimation

Abstract

Space domain awareness using current human-in-the-loop methods is becoming decreasingly viable. This work presents an approach to sensor network management, maneuver detection, and adaptive estimation for tracking many non-maneuvering and multiple maneuvering satellites with a space object surveillance and identification (SOSI) network. The proposed method integrates a suboptimal partially observable Markov decision process (POMDP) with an unscented Kalman filter (UKF) to task sensors and maintain viable orbit estimates for all targets. The method also implements autonomous maneuver detection based on the innovations squared metric. Once detected, the network instantiates a multiple model adaptive estimation (MMAE) filter with various possible maneuvers. This study implemented both a static multiple model (SMM) filter and an interacting multiple model (IMM) filter in order to compare the two methods. When comparing the two multiple model filters' responsiveness and accuracy in this framework, it is shown that the IMM marginally outperforms the SMM for a substantial, impulsive maneuver in three different orbital regimes.