Scholarlink

<p>A novel geometric collision risk assessment method is introduced that determines the unique minimum separation distance between two covariance ellipsoids representing the positional uncertainty of space objects. By reformulating the problem to find the unique point at which the normal vectors of the uncertainty envelopes are parallel, we eliminate the need for complex sextic polynomial root-finding. The methodology employs a characteristic bisection algorithm generalizable to <i>n</i> dimensions, guaranteeing a real solution with <i>O</i>(1) complexity regardless of dimensionality. This provides a critical enabling technology for next-generation AI-enabled space traffic management (STM), facilitating both real-time autonomous decision-making and the integration of high-dimensional non-spatial risk factors.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Guaranteed Geometric Approach to Covariance-Ellipsoid Separation for Satellite Collision Avoidance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Bao Michael Nguyen,
  • Dale F. Reding

摘要

A novel geometric collision risk assessment method is introduced that determines the unique minimum separation distance between two covariance ellipsoids representing the positional uncertainty of space objects. By reformulating the problem to find the unique point at which the normal vectors of the uncertainty envelopes are parallel, we eliminate the need for complex sextic polynomial root-finding. The methodology employs a characteristic bisection algorithm generalizable to n dimensions, guaranteeing a real solution with O(1) complexity regardless of dimensionality. This provides a critical enabling technology for next-generation AI-enabled space traffic management (STM), facilitating both real-time autonomous decision-making and the integration of high-dimensional non-spatial risk factors.