Scholarlink

<p>This work examines alternative strategies for incorporating parameter uncertainty in the Constrained Admissible Region (CAR) generation algorithm to address practical issues encountered in large scale Multi-Hypothesis Filter applications. This work first reproduces and expands upon the differential inflation approach developed by Worthy et al. to include both prevalent CAR constraint bounds on orbital eccentricity and semi-major axis. Strategies are developed to reduce intrinsic numerical ill-conditioning of this approach reported in the original literature. To examine the higher order moments of the parameter-uncertainty inflated CAR, this work also adapts the Unscented Transform (UT) to measure nonlinear distortions in the CAR boundary using jointly-parameterized solution contours. The accuracy and numerical efficiency of the above approaches are then compared against Monte-Carlo simulations in both the LEO and GEO orbital regimes. This comparison yields the following key conclusions: (1) the size and shape of the CAR is non-negligibly affected by the inclusion of parameter error in both LEO and GEO regimes, (2) the UT approach reproduces the nonlinear inflation of the CAR more accurately than the differential approach, but is more computationally intensive, and (3) results from these two approaches mainly diverge near saddle points in the constraint functions. These results highlight the importance of parameter error in admissible region generation and present multiple approaches to combine the respective robustness and computational efficiency benefits of the Probabilistic Admissible Region and CAR approaches in practical applications.</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

Robust Strategies for Incorporating Parameter Uncertainty in Constrained Admissible Regions

  • 期刊论文

  • 发表日期：

    2026年2月27日

查看全文
  • Thomas L. Dearing,
  • Jacob D. Griesbach,
  • Piyush M. Mehta

摘要

This work examines alternative strategies for incorporating parameter uncertainty in the Constrained Admissible Region (CAR) generation algorithm to address practical issues encountered in large scale Multi-Hypothesis Filter applications. This work first reproduces and expands upon the differential inflation approach developed by Worthy et al. to include both prevalent CAR constraint bounds on orbital eccentricity and semi-major axis. Strategies are developed to reduce intrinsic numerical ill-conditioning of this approach reported in the original literature. To examine the higher order moments of the parameter-uncertainty inflated CAR, this work also adapts the Unscented Transform (UT) to measure nonlinear distortions in the CAR boundary using jointly-parameterized solution contours. The accuracy and numerical efficiency of the above approaches are then compared against Monte-Carlo simulations in both the LEO and GEO orbital regimes. This comparison yields the following key conclusions: (1) the size and shape of the CAR is non-negligibly affected by the inclusion of parameter error in both LEO and GEO regimes, (2) the UT approach reproduces the nonlinear inflation of the CAR more accurately than the differential approach, but is more computationally intensive, and (3) results from these two approaches mainly diverge near saddle points in the constraint functions. These results highlight the importance of parameter error in admissible region generation and present multiple approaches to combine the respective robustness and computational efficiency benefits of the Probabilistic Admissible Region and CAR approaches in practical applications.