NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

作者：Vila Valls Jordi^*; Closas Pau

来源：EURASIP Journal on Advances in Signal Processing, 2017, 2017(1): 62.

DOI：10.1186/s13634-017-0498-4

• Robust Bayesian inference
• Gaussian filtering and smoothing
• NLOS mitigation
• Skew t-distributed measurement noise
• Indoor localization
• Monte Carlo integration

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 marginalized using Monte Carlo sampling. Numerical results are provided to show the performance improvement of the proposed approach.

• 出版日期2017-8-29
• 单位东北大学