...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of aerospace engineering >Linear Covariance-Based Optimal Sensor Selection for GN&C System Using Second-Order Cone Programming
【24h】

Linear Covariance-Based Optimal Sensor Selection for GN&C System Using Second-Order Cone Programming

机译：基于二阶锥规划的GN＆C系统基于线性协方差的最优传感器选择

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A novel optimal sensor selection approach is developed in this paper. The key innovation of this work is in formulating the stochastic optimal sensor selection problem as a second-order convex program. The approach quickly determines the required optimal sensor specifications that meet mission navigation and trajectory dispersion requirements with the lowest sensor cost. The proposed approach combines linear covariance analysis with convex optimization to describe and solve the optimal sensor selection problem. First, the trajectory dispersion of the closed-loop guidance, navigation, and control (GN&C) system based on sensor specifications is modeled using linear covariance analysis theory. Then, the linear covariance propagation and update equations are used to formulate an optimal sensor selection problem using the Kronecker product. Second-order cone programming with successive approximation techniques are used to solve the established problem. Finally, a simple nonlinear closed-loop GN&C system is investigated, and the capabilities of the proposed approach are demonstrated. The simulations show that the optimal sensor selection problem can be described and solved efficiently using the proposed approach.
机译：本文提出了一种新颖的最优传感器选择方法。这项工作的关键创新在于将随机最优传感器选择问题表述为二阶凸程序。该方法可快速确定所需的最佳传感器规格，从而以最低的传感器成本满足任务导航和轨迹分散要求。所提出的方法将线性协方差分析与凸优化相结合，以描述和解决最优传感器选择问题。首先，使用线性协方差分析理论对基于传感器规格的闭环制导，导航和控制（GN＆C）系统的轨迹分散进行建模。然后，使用Kronecker乘积，使用线性协方差传播和更新方程式来制定最佳传感器选择问题。使用具有逐次逼近技术的二阶锥规划来解决已建立的问题。最后，研究了一个简单的非线性闭环GN＆C系统，并证明了该方法的功能。仿真表明，使用所提出的方法可以描述和有效地解决最优传感器选择问题。

著录项

  • 来源
    《Journal of aerospace engineering》 |2019年第4期|04019024.1-04019024.9|共9页
  • 作者

    Jin Kai; Geller David; Luo Jianjun;

  • 作者单位

    Northwestern Polytech Univ, Sch Astronaut, Natl Key Lab Aerosp Flight Dynam, Xian 710072, Shaanxi, Peoples R China;

    Utah State Univ, Dept Mech & Aerosp Engn, Logan, UT 84322 USA;

    Northwestern Polytech Univ, Sch Astronaut, Natl Key Lab Aerosp Flight Dynam, Xian 710072, Shaanxi, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号