---
title: "Eclipse Transient OD | Truman DeWalch"
description: "Orbit determination using eclipse transients with statistical atmospheric models"
url: "https://trumandewalch.dev/research/eclipse-transient-od"
route: "/research/eclipse-transient-od"
---

Prior Research

# Eclipse Transient Orbit Determination

Leveraging statistical atmospheric models for autonomous navigation

**Timeline:** Sept 2022 – Oct 2023

**Publication:** AIAA SciTech 2024

**Status:** Published

Eclipse transient as an orbit-determination observable at the terminator.

## Summary

- Eclipse transients as observables for autonomous navigation.
- UKF estimation with statistical atmosphere modeling.
- Published AIAA SciTech 2024 results.

## Overview

When a satellite crosses into or out of Earth's shadow, it experiences measurable changes in thermal state, power generation, and sensor readings. These **eclipse transients** contain geometric information about the satellite's orbit—information that can be exploited for autonomous navigation without ground station communication.

This research developed a modular simulation framework for modeling eclipse transients and demonstrated how **Unscented Kalman Filtering (UKF)** can estimate orbital state from these measurements. A key contribution was incorporating **statistical atmospheric models** derived from real-world climate data, capturing variability in atmospheric refraction that deterministic models miss.

## The Problem

Traditional orbit determination relies on ground-based tracking or GNSS. For missions requiring autonomous navigation—whether due to communication constraints, operational resilience, or cost— alternative observables are needed. Eclipse boundaries provide a geometric constraint: the satellite must lie on a cone defined by the Sun-Earth geometry. By observing *when* eclipse transitions occur, we can constrain the orbit.

However, Earth's atmosphere complicates matters. Atmospheric refraction bends light near the limb, and density variations cause the effective shadow boundary to fluctuate. Ignoring this uncertainty degrades navigation accuracy significantly.

## Technical Approach

- **Eclipse Driver** Modular Python architecture for simulating eclipse entry/exit with configurable measurement sampling and noise models
- **Statistical Atmosphere** Climate-derived density profiles capture real-world variability rather than fixed models like US Standard Atmosphere
- **UKF State Estimation** Unscented Kalman Filter propagates uncertainty through nonlinear eclipse geometry without linearization errors
- **Sensitivity Analysis** Quantified how atmospheric model uncertainty affects navigation accuracy across different orbital regimes

## Key Parameters

Measurement Config · Sample period, duration, noise sigma, logistic softness for shadow transition modeling

Filter Config · Process/measurement covariance (6×6), UKF tuning parameters (α=0.3, β=2.0, κ=0.0)

Atmospheric Model · Statistical density profiles from climate data with atmospheric refraction computation

## Key Contributions

- Modular Python software architecture enabling rapid iteration on eclipse simulation configurations
- Statistical atmospheric model that captures real-world density variability from climate data
- UKF-based state estimation framework for eclipse transient measurements
- Quantitative analysis of filter sensitivity to atmospheric model uncertainty

## Publication

### "Enhancing Eclipse Transient Orbit Determination Methods with Statistical Atmospheric Models"

DeWalch, T., Fitzgerald, R.

AIAA SciTech Forum 2024

Presents the eclipse transient simulation framework, demonstrates UKF-based orbit determination, and quantifies the benefit of statistical atmospheric modeling over deterministic approaches.

## Tools & Technologies

Python · NumPy · SciPy · UKF · Statistical Atmospheres · Eclipse Modeling · Autonomous Navigation

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