...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Signal Processing, IEEE Transactions on >Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons
【24h】

Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

机译：动态非线性建模中用于密度估计的Dirichlet过程混合物：在城市峡谷GPS定位中的应用

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

In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions.
机译：在全球定位系统（GPS）中，当在视距（LOS）环境中从卫星接收信号时，经典的定位算法假定伪距误差分布为高斯分布。这种假设在某种意义上是非常严格的，因为由于多径/掩蔽效应，总是在受约束的环境中，特别是在城市峡谷中，导致具有未知分布形式的伪距测量中的随机误差。为了确保高精度定位，在这些情况下需要对观察误差进行良好的估计。为了解决这个问题，引入了基于Dirichlet过程混合（DPM）的有吸引力的灵活贝叶斯非参数噪声模型。由于所考虑的定位问题涉及非高斯性和非线性因素，此外，还应在线进行处理，因此建议的建模方案在联合状态/参数估计问题中的适用性由高效的Rao-Blackwellized粒子滤波器处理（ RBPF）。在数据分析任务中说明了我们的方法，该任务处理联合估计基于全球导航卫星系统（GNSS）的定位环境中的车辆位置和伪距误差，在这种情况下，由于接收条件恶劣，GPS信息可能不准确。

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