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对于一类非线性估计问题的多重线性化来说,递归批量更光滑:用于多传感器导航数据融合的应用.

Oleg Stepanov1, Alexey Isaev1, Elena Dranitsyna1

  • 1Faculty of Control Systems and Robotic, ITMO University, 197101 St. Petersburg, Russia.

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概括
此摘要是机器生成的。

两个新的算法,即递归代批量线性化平滑器 (RI-BLS) 和递归代批量多重线性化平滑器 (RI-BMLS),有效地解决了导航系统中复杂的非线性过问题. 这些方法比现有技术提供了显著的计算优势.

关键词:
贝叶斯的方法是贝叶斯的方法.批量算法 批量算法 批量算法扩展的卡尔曼过器过器的过器是一个过器.代算法是一种代算法.地图辅助导航的导航.多重线性化的多重线性化多传感器导航数据融合没有回归性计划的计划.颗粒过器 颗粒过器递归的计划是递归的计划.窒息 窒息 窒息 窒息

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科学领域:

  • 导航系统工程 导航系统工程
  • 非线性估计理论非线性估计理论
  • 计算数学是指计算数学.

背景情况:

  • 来自多个导航传感器的数据融合带来了非线性过的挑战.
  • 后方概率密度函数 (PDF) 随着测量的累积而从多极端演变为单极端.
  • 序列式蒙特卡洛方法提供了潜在的准确性,但对于实时应用来说,它们在计算上是密集的.
  • 传统的递归算法,如扩展的卡尔曼过器,对于这些问题往往无法操作.

研究的目的:

  • 开发用于导航系统中非线性过的计算效率高的算法.
  • 解决现有方法的局限性,包括连续的蒙特卡洛和传统的递归过器.
  • 提出新的解决方案,克服当前方法的缺点.

主要方法:

  • 开发一个递归代批量线性化平滑器 (RI-BLS),使用递归程序代处理所有累积的测量.
  • 介绍一个递归代批次多重线性化光滑器 (RI-BMLS),使用一组并行RI-BLS算法.
  • 通过方法示例和地图辅助导航问题进行验证.

主要成果:

  • 提出的RI-BLS和RI-BMLS算法有效地解决了所考虑的非线性过问题的类别.
  • 与粒子过器相比,RI-BLS显示了超过15倍的计算简单性.
  • 与粒子过器相比,RI-BMLS显示了超过20倍的计算简单性,估计准确度相似.

结论:

  • RI-BLS和RI-BMLS为导航系统中的非线性过提供了高效和准确的解决方案,特别是用于数据融合.
  • 这些算法克服了与现有方法相关的计算复杂性和不可操作性问题.
  • 提出的方法为实时导航系统的实施提供了切实可行的替代方案.