astra-core-engine 3.3.0

Autonomous Space Traffic Risk Analyzer - Computation Engine with native TLE, OMM, and Space-Track support

ASTRA-Core v3.3.0 (Autonomous Space Traffic Risk Analyzer) 🛰️

The high-performance mathematical foundation for space situational awareness.

ASTRA-Core is a rigorous Python astrodynamics engine for aerospace engineers, researchers, and developers. It propagates large catalogs, screens conjunctions, estimates collision probability, and predicts ground passes—using both legacy TLE and modern OMM data end-to-end.

📖 API reference: Read the Docs

🧠 How the math works: KNOWMORE.md — TLEs, OMM, SGP4, spatial screening, P_c, Cowell forces, and what the models assume.

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Orbital data: TLE vs OMM

ASTRA-Core supports both the legacy TLE format and the CCSDS OMM (Orbit Mean-Elements Message). Physics APIs accept either via the unified SatelliteState type.

Feature	SatelliteTLE (legacy)	SatelliteOMM (modern ★)
Source format	69-character text lines	JSON key-value pairs
Mass (kg)	Not in format	mass_kg
Radar cross-section (m²)	Not in format	rcs_m2
Ballistic coefficient	Not in format	cd_area_over_mass
Collision radius	Estimated default	From RCS / metadata when present
Parsing	Checksums, fixed columns	JSON — structured fields
Backwards compatible	Yes	Yes (same pipelines)
Recommended for	Legacy workflows	New projects

Tip: Prefer OMM when you care about drag, realistic cross-sections, or conjunction risk—the extra metadata flows into screening and Cowell without extra glue code.

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Data sources: CelesTrak vs Space-Track

	CelesTrak	Space-Track.org
Account	Not required	Free registration
Formats	TLE + OMM JSON	TLE + OMM JSON
Coverage	Large public catalogs	Authoritative catalog
Updates	Periodic	Periodic (per provider)
Notes	Rate limits may apply	Session auth via env vars

CelesTrak (no account)

import astra

tles = astra.fetch_celestrak_group("starlink")
omms = astra.fetch_celestrak_group_omm("starlink")

Requests identify the client as ASTRA-Core/<version>.

Space-Track (environment variables)

Never hardcode passwords. Set once per machine:

# Windows (Command Prompt — restart shell after setx)
setx SPACETRACK_USER [e-mail]
setx SPACETRACK_PASS yourpassword

# Linux / macOS (~/.bashrc or ~/.zshrc)
export SPACETRACK_USER=[e-mail]
export SPACETRACK_PASS=yourpassword

import astra

starlinks = astra.fetch_spacetrack_group("starlink")
catalog = astra.fetch_spacetrack_active()

If credentials are missing, ASTRA raises a clear error with setup hints.

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Key capabilities

• Dual format (TLE + OMM): One API surface for parsing, propagation, filtering, and conjunctions.
• SGP4 at scale: Vectorized propagation (propagate_many, generators) with UT1-aware handling where ephemeris data are available.
• Cowell propagation: Dormand–Prince integration with J₂–J₄, empirical drag (space weather), Sun/Moon third-body gravity (JPL DE421), optional solar radiation pressure with cylindrical Earth shadow (umbra only; no penumbra), and 7-DOF finite burns with mass flow.
• Conjunction screening: KD-tree prefilter over time steps, spline refinement for TCA, dynamic effective radius from metadata when available.
• Collision probability: Analytical (Chan/Foster lineage) and 6D Monte Carlo paths when full covariances are supplied; CDM XML import via hardened parsing (defusedxml).
• Catalog ingestion: CelesTrak and Space-Track helpers plus local OMM files.
• Pass prediction: TEME → ground observer pipeline (ENU), coarse grid + refinement for AOS/TCA/LOS.
• Optional 3D plots: Interactive Plotly figures via the [viz] extra—core install stays lean for servers and CI.

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Installation

Default (core physics, no Plotly):

pip install astra-core-engine

With 3D trajectory plotting:

pip install "astra-core-engine[viz]"

From source (development + tests):

git clone https://github.com/ISHANTARE/ASTRA.git
cd ASTRA
pip install -e ".[test]"

Requires Python 3.10+. Core dependencies include NumPy, SciPy, Skyfield, SGP4, Requests, Numba, and defusedxml.

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Using the library responsibly

ASTRA-Core implements widely used models suitable for research, education, integration prototypes, and operations-style workflows when you understand the assumptions. It is not a certified conjunction or mission-closure product by itself—validate against your own requirements and reference tools if needed.

Topic	What to know
Sun/Moon ephemeris	Default kernel is DE421 (roughly 1900–2050). Very long or future-dated studies may need another ephemeris (e.g. DE440) and your own validation.
Atmosphere	Empirical Jacchia-class density, not NRLMSISE. Not intended for detailed re-entry or the densest LEO regimes alone.
SRP	Simple cannonball model; optional cylindrical shadow (full sun or full shadow in umbra). Penumbra is not modeled.
P_c	Depends on covariance quality. Built-in estimate_covariance() is a rough heuristic—for serious thresholds, use CDM-class covariances. Turn on strict mode to avoid silent fallbacks.
Monte Carlo P_c	Uses a straight-line relative-motion model per sample; very slow co-orbital encounters need careful interpretation and finer time sampling.
Catalog quality	Stale or poor elements dominate error—always check epoch and data source.

Strict mode: astra.set_strict_mode(True) or astra.config.ASTRA_STRICT_MODE = True makes many missing-data paths raise instead of warn-and-continue—recommended when building tools that must not guess.

More detail: KNOWMORE.md and the Limitations page on Read the Docs.

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Quickstart

TLE workflow

import astra
import numpy as np

active_catalog = astra.fetch_celestrak_active()
objects = [astra.make_debris_object(tle) for tle in active_catalog]
leo_only = astra.filter_altitude(objects, min_km=200, max_km=2000)

sources = [obj.source for obj in leo_only]
times_jd = leo_only[0].source.epoch_jd + np.arange(0, 120, 5.0) / 1440.0
trajectories = astra.propagate_many(sources, times_jd)

events = astra.find_conjunctions(
    trajectories,
    times_jd=times_jd,
    elements_map={obj.source.norad_id: obj for obj in leo_only},
    threshold_km=5.0,
)

OMM workflow (recommended for new code)

import astra
import numpy as np

omm_catalog = astra.fetch_celestrak_active_omm()
# Or: omm_catalog = astra.load_omm_file("catalog.json")

objects = [astra.make_debris_object(omm) for omm in omm_catalog]
leo_only = astra.filter_altitude(objects, min_km=200, max_km=2000)

sources = [obj.source for obj in leo_only]
times_jd = leo_only[0].source.epoch_jd + np.arange(0, 120, 5.0) / 1440.0
trajectories = astra.propagate_many(sources, times_jd)

events = astra.find_conjunctions(
    trajectories,
    times_jd=times_jd,
    elements_map={obj.source.norad_id: obj for obj in leo_only},
    threshold_km=5.0,
)
print(f"Found {len(events)} conjunction events.")

Space-Track catalog

import astra

catalog = astra.fetch_spacetrack_active()
print(f"Loaded {len(catalog)} satellites.")

Optional: Plotly ([viz] installed)

from astra import plot_trajectories

fig = plot_trajectories({"25544": positions_array})

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Library API cheatsheet

Functions are available from the astra namespace.

CelesTrak

Function	Returns
fetch_celestrak_active()	list[SatelliteTLE]
fetch_celestrak_group(group)	list[SatelliteTLE]
fetch_celestrak_comprehensive()	list[SatelliteTLE]
fetch_celestrak_active_omm()	list[SatelliteOMM]
fetch_celestrak_group_omm(group)	list[SatelliteOMM]
fetch_celestrak_comprehensive_omm()	list[SatelliteOMM]

Space-Track

Function	Returns
fetch_spacetrack_group(group, format=...)	OMM (default) or TLE
fetch_spacetrack_active()	Active catalog
fetch_spacetrack_satcat()	SATCAT-style records
spacetrack_logout()	End session

OMM

• parse_omm_json(text) → list[SatelliteOMM]
• parse_omm_record(dict) → SatelliteOMM
• load_omm_file(path) → list[SatelliteOMM]
• validate_omm(dict) → bool

TLE

• load_tle_catalog(lines) → list[SatelliteTLE]
• parse_tle(name, l1, l2) → SatelliteTLE
• validate_tle(name, l1, l2) → bool

Filtering & debris

• make_debris_object(source) — SatelliteTLE or SatelliteOMM
• filter_altitude, filter_region, filter_time_window, apply_filters, catalog_statistics

Propagation

• propagate_orbit, propagate_many, propagate_many_generator, propagate_trajectory, ground_track
• propagate_cowell — numerical Cowell + DragConfig

Conjunctions & probability

• find_conjunctions, closest_approach, distance_3d
• compute_collision_probability, compute_collision_probability_mc
• estimate_covariance, propagate_covariance_stm, rotate_covariance_rtn_to_eci
• parse_cdm_xml

Space weather & ephemeris helpers

• get_space_weather, load_space_weather, atmospheric_density_empirical
• sun_position_de, moon_position_de, etc.

Visibility

• passes_over_location, visible_from_location

Utilities & config

• convert_time, vincenty_distance, orbit_period, orbital_elements
• set_strict_mode, astra.config.ASTRA_STRICT_MODE

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Examples

Script	Topic
examples/01_basic_conjunctions.py	Collision screening pipeline
examples/02_visualize_swarm.py	3D LEO constellation plot
examples/03_ground_station_visibility.py	Pass prediction
examples/04_omm_pipeline.py	OMM end-to-end
examples/05_compare_tle_omm.py	TLE vs OMM
examples/06_spacetrack_pipeline.py	Space-Track

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Changelog

Release notes: CHANGELOG.md.

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How to cite

@software{Tare_ASTRA_2026,
  author = {Tare, Ishan},
  title = {ASTRA: Autonomous Space Traffic Risk Analyzer},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ISHANTARE/ASTRA}},
  version = {3.3.0}
}

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Author

ASTRA Team — Ishan Tare

© 2026 ASTRA Project · MIT License