• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >A Particle Filter Approach to Approximate Posterior Cramer-Rao Lower Bound: The Case of Hidden States
【24h】

A Particle Filter Approach to Approximate Posterior Cramer-Rao Lower Bound: The Case of Hidden States

机译：粒子滤波方法近似近似后克拉莫-拉下界：隐藏状态的情况

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

The posterior Cramer-Rao lower bound (PCRLB) derived in [1] provides a bound on the mean square error (MSE) obtained with any nonlinear state filter. Computing the PCRLB involves solving complex, multi-dimensional expectations, which do not lend themselves to an easy analytical solution. Furthermore, any attempt to approximate it using numerical or simulation-based approaches require a priori access to the true states, which may not be available, except in simulations or in carefully designed experiments. To allow recursive approximation of the PCRLB when the states are hidden or unmeasured, a new approach based on sequential Monte-Carlo (SMC) or particle filters (PFs) is proposed. The approach uses SMC methods to estimate the hidden states using a sequence of the available sensor measurements. The developed method is general and can be used to approximate the PCRLB in nonlinear systems with non-Gaussian state and sensor noise. The efficacy of the developed method is illustrated on two simulation examples, including a practical problem of ballistic target tracking at reentry phase.
机译：文献[1]中得出的后克雷默-拉奥下界（PCRLB）提供了用任何非线性状态滤波器获得的均方误差（MSE）的界限。计算PCRLB涉及解决复杂的多维期望，而这些期望并不适合于简单的分析解决方案。此外，使用数字或基于仿真的方法对它进行近似的任何尝试都需要先验地获取真实状态，除非在仿真或经过精心设计的实验中，否则可能无法获得真实状态。为了在状态被隐藏或无法测量时允许PCRLB的递归近似，提出了一种基于顺序蒙特卡洛（SMC）或粒子滤波器（PF）的新方法。该方法使用SMC方法通过一系列可用的传感器测量值来估计隐藏状态。所开发的方法是通用的，可用于在具有非高斯状态和传感器噪声的非线性系统中近似PCRLB。在两个仿真示例上说明了该方法的有效性，其中包括在重入阶段跟踪弹道目标的实际问题。

著录项

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