• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>European Navigation Conference >Regularized estimation for GNSS positioning in multipathon-line-of-sight environments
【24h】

Regularized estimation for GNSS positioning in multipathon-line-of-sight environments

机译：在多路径/非视线环境中对GNSS定位的常规估计

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Considered as the free accessible and suitable solution for positioning in urban areas, Global Navigation Satellite Systems (GNSS) have been widely used these recent years in a wide spectrum of applications. However, signal blockage, non-line-of-sight (NLOS) multipath interferences and signal degradation affect the system performance and represent the major hurdles of GNSS in it course of adoption as a main localization technology in urban environments. Many approaches have been employed to constructively use these degraded signals in order to reduce positioning errors. Following this vision, we propose in this paper a joint estimation method of the position and the bias for measurement correction. This formulation leads to an ill-conditioned estimation problem. In this work, we apply a regularized robust estimation framework to this problem of NLOS mitigation for GNSS positioning in harsh areas. We derive the optimal regularization matrix by minimizing the total Mean Square Errors (MSE) of the considered model. The performance of the proposed method is assessed using real GNSS data collected in a dense urban area in Toulouse City, showing improvements in comparison to some existing methods.
机译：全球导航卫星系统（GNSS）被认为是在城市地区定位的免费便捷解决方案，近年来在许多应用中得到了广泛的应用。但是，信号阻塞，非视距（NLOS）多径干扰和信号衰减会影响系统性能，并且在被用作城市环境中的主要定位技术的过程中，代表了GNSS的主要障碍。为了减少定位误差，已经采用了许多方法来建设性地使用这些降级的信号。遵循这一愿景，我们在本文中提出了一种位置估计和位置偏差的联合估计方法。这种表述导致病态的估计问题。在这项工作中，我们将针对此GNSS定位在恶劣区域中的NLOS缓解问题应用正则化的鲁棒估计框架。我们通过最小化所考虑模型的总均方误差（MSE）得出最佳正则化矩阵。使用在图卢兹市一个人口稠密的城市地区收集的实际GNSS数据评估了该方法的性能，与现有方法相比，该方法显示出了改进。

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