A Bayesian Approach to Low-Thrust Maneuvering Spacecraft Tracking

TL;DR

This paper introduces a Bayesian tracking algorithm for maneuvering spacecraft that effectively uses a multivariate Laplace distribution for thrust profiles, enabling accurate tracking with minimal observations and high thrust mismatch tolerance.

Contribution

The paper presents a novel Bayesian tracking method with a multivariate Laplace prior and a k-nearest neighbor ensemble Gaussian mixture filter for maneuvering spacecraft.

Findings

Effective tracking with fewer than two observations per day.

Robust performance despite up to 200-fold mismatch in thrust magnitude.

Validated through multiple Monte Carlo simulations across scenarios.

Abstract

Bayesian estimation with an explicit transitional prior is required for a tracking algorithm to be embedded in most multi-target tracking frameworks. This paper describes a novel approach capable of tracking maneuvering spacecraft with an explicit transitional prior and in a Bayesian framework, with fewer than two observations passes per day. The algorithm samples thrust profiles according to a multivariate Laplace distribution. It is shown that multivariate Laplace distributions are particularly suited to track maneuvering spacecraft, leading to a log probability function that is almost linear with the thrust. Principles from rare event simulation theory are used to propagate the tails of the distribution. Fast propagation is enabled by multi-fidelity methods. Because of the diffuse transitional prior, a novel k-nearest neighbor-based ensemble Gaussian mixture filter is developed and used.The method allows Bayesian tracking of maneuvering spacecraft for several scenarios with fewer than two measurement passes per day, and with a mismatch between the true and expected thrust magnitude of up to a factor of 200. The validity domain and statistical significance of the method are shown by simulation through several Monte Carlo trials in different scenarios and with different filter settings.