# A Multifidelity Approach to Robust Orbit Determination

## TL;DR

This paper introduces a multifidelity algorithm that enhances orbit determination robustness by refining initial estimates, detecting outliers, and efficiently propagating uncertainties using differential algebra and domain splitting techniques.

## Contribution

The paper presents a novel multifidelity approach combining differential algebra and automatic domain splitting to improve orbit determination robustness and outlier detection.

## Key findings

- Effective outlier detection in orbit data
- Improved initial orbit estimates with reduced uncertainty
- Validated on synthetic and real TAROT network data

## Abstract

This paper presents an algorithm for the preprocessing of observation data aimed at improving the robustness of orbit determination tools. Two objectives are fulfilled: obtain a refined solution to the initial orbit determination problem and detect possible outliers in the processed measurements. The uncertainty on the initial estimate is propagated forward in time and progressively reduced by exploiting sensor data available in said propagation window. Differential algebra techniques and a novel automatic domain splitting algorithm for second-order Taylor expansions are used to efficiently propagate uncertainties over time. A multifidelity approach is employed to minimize the computational effort while retaining the accuracy of the propagated estimate. At each observation epoch, a polynomial map is obtained by projecting the propagated states onto the observable space. Domains that do no overlap with the actual measurement are pruned thus reducing the uncertainty to be further propagated. Measurement outliers are also detected in this step. The refined estimate and retained observations are then used to improve the robustness of batch orbit determination tools. The effectiveness of the algorithm is demonstrated for a geostationary transfer orbit object using synthetic and real observation data from the TAROT network.