lunar-orbit-determination  |  orbit determination  |  estimation algorithms  |  scientific Python

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What I Work On

I develop computational tools for spacecraft navigation research. My current work is centered on lunar orbit determination: trajectory propagation, ground-station visibility, synthetic tracking measurements, estimator diagnostics, and reproducible comparison campaigns.

The technical emphasis is not only on getting an estimate, but on understanding why an estimator succeeds or fails under sparse geometry, interrupted tracking, nonlinear dynamics, and imperfect measurement information.

Active Repository

lunar-orbit-determination

A Python framework and native PyQt5 desktop application for lunar orbit determination experiments.

Layer	What it contains
Dynamics	Moon-centered propagation, third-body perturbations, STM integration
Geometry	SPICE frames, Earth-fixed stations, elevation masks, lunar occultation
Measurements	Range, azimuth, elevation, range-rate, simplified counted Doppler
Estimation	BLS-LM, SRIF, SR-UKF, covariance and consistency diagnostics
Experiments	Monte Carlo, long-duration arcs, fragmented visibility, Doppler cases
Interface	Local PyQt5 app for scenario setup, run monitoring, plots, and comparison

Technical Map

truth trajectory
      |
      v
visibility + occultation  ->  tracking arcs
      |
      v
synthetic observables     ->  range / angles / range-rate / counted Doppler
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      v
estimation pipeline       ->  BLS-LM / SRIF / SR-UKF
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      v
diagnostics               ->  error, covariance, NIS, condition number, runtime

Current Engineering Questions

• How do batch and sequential estimators behave when lunar tracking is fragmented?
• When does weak station geometry dominate estimator performance?
• How should synthetic measurements be upgraded from instantaneous geometry to light-time-aware observables?
• Which diagnostics best explain estimator failure: residuals, covariance, NIS, rank, condition number, or arc structure?
• How can research scripts be packaged into a local tool that is useful for scenario design and result inspection?

Working Stack

Area	Tools
Scientific Python	NumPy, SciPy, pandas, Matplotlib
Astrodynamics	SPICE / spiceypy, reference frames, state propagation
Estimation	least squares, square-root information filtering, unscented Kalman filtering
Testing	pytest, seeded regression cases, finite-difference checks
UI	PyQt5, Qt Designer, local desktop tooling
Documentation	Markdown, LaTeX, reproducible experiment outputs

Repository Signal

Direction

My next improvements are focused on measurement realism and estimator defensibility:

• one-way light-time corrected range / azimuth / elevation observables
• clearer separation between geometric range-rate and counted Doppler
• stronger residual/model consistency checks
• better documentation of model limitations and assumptions
• cleaner public-facing examples for reproducing comparison results

Contact

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_{Research software for orbit determination, estimator comparison, and lunar tracking analysis.}