Star Tracker Misalignment Compensation in Deep Space Navigation Through Model-Based Estimation

TL;DR

This paper introduces an adaptive, model-based estimation framework that accurately compensates star tracker misalignment in deep space navigation, improving attitude estimation in GPS-denied environments.

Contribution

It develops a robust Bayesian MMAE framework with a novel grid refinement strategy for in-flight star tracker misalignment calibration.

Findings

Reduces final misalignment RMSE to arcsecond-level accuracy

Demonstrates robustness against hypothesis convergence issues

Enhances spacecraft navigational autonomy in GPS-denied environments

Abstract

This work presents a novel adaptive framework for simultaneously estimating spacecraft attitude and sensor misalignment. Uncorrected star tracker misalignment can introduce significant pointing errors that compromise mission objectives in GPS-denied environments. To address this challenge, the proposed architecture integrates a Bayesian Multiple-Model Adaptive Estimation (MMAE) framework operating over an N x N x N 3D hypothesis grid. Each hypothesis employs a 9-state Multiplicative Extended Kalman Filter (MEKF) to estimate attitude, angular velocity, and gyroscope bias using TRIAD-based vector measurements. A key contribution is the development of a robust grid refinement strategy that uses hypothesis diversity and weighted-mean grid centering to prevent the premature convergence commonly encountered in classical, dominant model-based refinement triggers. Extensive Monte Carlo simulations demonstrate that the proposed method reduces the final misalignment RMSE relative to classical approaches, achieving arcsecond-level accuracy. The resulting framework offers a computationally tractable and statistically robust solution for in-flight calibration, enhancing the navigational autonomy of resource-constrained spacecraft.