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Hussein_1.pdf
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🔗 Source: amostech.com
📊 Size: 1.62 MB
👤 Author: Weston Faber
⬇️ Downloads: 567

Summary

The potential for breakup events and space object collisions in space is increasing, creating hazardous debris clouds. Providing timely and accurate Space Situational Awareness (SSA) data is crucial to protect space assets and operations. The space object tracking problem is nonlinear, making linear filtering techniques like the Kalman filter unsuitable. This paper proposes a solution using Sequential Monte Carlo (SMC) methods for nonlinear filtering and hypothesis management, relaxing simplifying assumptions and avoiding heuristic methods. The goal is to detail this solution and discuss computational limitations that hinder analysis of large breakup events. The methodology utilizes a Probabilistic Admissible Region (PAR) for fragment PDF initialization and a Random Finite Set (RFS) based technique called R-FISST for multi-object filtering, allowing for choice of continuous filter and keeping the Data Association Problem computationally tractable.

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Description

The potential for breakup events and space object collisions in space is increasing, creating hazardous debris clouds.

Technical Information

  • File Format: PDF
  • File Size: 1.62 MB
  • Pages: 13
  • Language: EN
  • Author: Weston Faber
  • Total Downloads: 567
  • Last Updated: 1 week ago

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