NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing
作者: Jordi Vilà-VallsPau Closas
作者单位: 1Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA)
2Northeastern University
刊名: EURASIP Journal on Advances in Signal Processing, 2017, Vol.2017 (1), pp.1-11
来源数据库: Springer Journal
DOI: 10.1186/s13634-017-0498-4
关键词: Robust Bayesian inferenceGaussian filtering and smoothingNLOS mitigationSkew t -distributed measurement noiseIndoor localizationMonte Carlo integration
英文摘要: Abstract(#br)One of the main challenges in indoor time-of-arrival (TOA)-based wireless localization systems is to mitigate non-line-of-sight (NLOS) propagation conditions, which degrade the overall positioning performance. The positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions can be modeled as a heavy-tailed skew t -distributed measurement noise. The main goal of this article is to provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. A key point is to take advantage of the conditionally Gaussian formulation of the skew t -distribution, thus being able to use computationally light Gaussian filtering and smoothing methods as the core of the new approach. The unknown non-Gaussian noise latent variables are...
全文获取路径: Springer  (合作)

  • smoothing 平滑
  • NLOS Natural Language Operating System
  • distributed 分布的
  • localization 定位
  • indoor 户内的
  • integration 集成
  • filtering 滤波
  • unknown 不知道的
  • noise 噪声
  • mitigation 水的软化