Scholarlink

<p>The growing threat of suborbital debris to ground infrastructure and spacecraft has made accurate prediction of re-entry trajectories and fallout points a critical issue. To tackle this problem, this paper proposes a geometric constraint-driven covariance propagation method combined with higher-order state evolution techniques to improve the precision of debris distribution assessment. Suborbital debris, typically located below the geostationary orbit, is affected by uncertainties such as atmospheric density, wind, and solar radiation pressure, which limit traditional prediction methods. To overcome these challenges, this study leverages deep randomization and covariance analysis, along with geometric constraints and polynomial Taylor semi-analytical expansions, to better model the nonlinear dynamics of debris behavior. Experimental results demonstrate that the proposed method significantly improves the accuracy of debris footprint prediction and provides new tools to reduce hazardous areas caused by suborbital debris.</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

Geometric-constrained covariance propagation for nonlinear dynamics in suborbital debris prediction

  • 期刊论文

  • 发表日期：

    2025年5月24日

查看全文
  • Min Chen,
  • Tongtong Zhao,
  • Wantong Chen

摘要

The growing threat of suborbital debris to ground infrastructure and spacecraft has made accurate prediction of re-entry trajectories and fallout points a critical issue. To tackle this problem, this paper proposes a geometric constraint-driven covariance propagation method combined with higher-order state evolution techniques to improve the precision of debris distribution assessment. Suborbital debris, typically located below the geostationary orbit, is affected by uncertainties such as atmospheric density, wind, and solar radiation pressure, which limit traditional prediction methods. To overcome these challenges, this study leverages deep randomization and covariance analysis, along with geometric constraints and polynomial Taylor semi-analytical expansions, to better model the nonlinear dynamics of debris behavior. Experimental results demonstrate that the proposed method significantly improves the accuracy of debris footprint prediction and provides new tools to reduce hazardous areas caused by suborbital debris.