Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation

TL;DR

This paper presents a Bayesian filtering framework using Sequential Monte Carlo methods for automatic detection and tracking of space object maneuvers in optical surveys, improving real-time space situational awareness.

Contribution

It introduces a novel stochastic hybrid system formulation combined with MCMC sampling and a new control distance metric for maneuver detection in space surveillance.

Findings

Effective maneuver detection in simulated scenarios

Outperforms moving horizon least-squares estimator

Reduces uncertainty in space object state estimation

Abstract

The state space representation of active resident space objects can be posed in the form of a stochastic hybrid system. Satellite maneuvers may be accounted for according to control cost or heuristical considerations, yet it is possible to jointly consider a combination of both. In this work, Sequential Monte Carlo filtering techniques are applied to the maneuvering target tracking problem in an optical survey scenario, where the maneuver control inputs are characterized in a Bayesian inference process. Due to the scarcity of data inherent to space surveillance and tracking, model switching probabilities are not estimated but derived from the ability of the state representation to fit incoming measurements. A Markov Chain Monte Carlo sampling scheme is used to explore the region assumed accessible to the object in terms of the hypothesized post-maneuver observation and a novel and efficient control distance metric. Results are obtained for a simulated optical survey scenario, and comparisons are drawn with respect to a moving horizon least-squares estimator. The proposed framework is proved to allow for a capable implementation of an automated online maneuver detection algorithm, thus contributing to the reduction of uncertainty in the state of active space objects.