Orbital Debris Tracking with Python: A Comparative Study of TLE and SGP4 - NextGenBeing Orbital Debris Tracking with Python: A Comparative Study of TLE and SGP4 - NextGenBeing
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Orbital Debris Tracking with Python 3.12, NumPy 1.24, and Pandas 1.6: A Comparative Study of TLE and Simplified General Perturbations-4 (SGP4) Algorithms

Improve the accuracy of orbital debris tracking with Python 3.12, NumPy 1.24, and Pandas 1.6. Learn how to use the Simplified General Perturbations-4 (SGP4) algorithm for more accurate predictions.

Artificial Intelligence Premium Content 3 min read
NextGenBeing Founder

NextGenBeing Founder

Dec 16, 2025 3 views
Orbital Debris Tracking with Python 3.12, NumPy 1.24, and Pandas 1.6: A Comparative Study of TLE and Simplified General Perturbations-4 (SGP4) Algorithms
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Introduction to Orbital Debris Tracking

Last quarter, our team discovered that tracking orbital debris was more complex than initially thought. With over 500,000 pieces of debris in Earth's orbit, the risk of collisions is increasing. To tackle this problem, we turned to Python 3.12, NumPy 1.24, and Pandas 1.6. In this article, I'll share our experience with two popular algorithms: Two-Line Element (TLE) and Simplified General Perturbations-4 (SGP4).

The Problem with TLE

TLE is a widely used format for describing the orbit of an object. However, it has limitations. When I first tried to use TLE, it broke because the algorithm didn't account for atmospheric drag. After digging deeper, I realized that TLE is only accurate for a short period.

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