Article

Journal of Engineering Research

, 4:15

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

UKF-based channel estimation and LOS/NLOS classification in UWB wireless networks

  • Mohamed A. LandolsiAffiliated withDepartment of Electrical Engineering, College of Engineering and Petroleum, Kuwait University Email author 
  • , Ali H. MuqaibelAffiliated withDepartment of Electrical Engineering, King Fahd University of Petroleum and Minerals
  • , Ali F. AlmutairiAffiliated withDepartment of Electrical Engineering, College of Engineering and Petroleum, Kuwait University

Abstract

The paper addresses Ultra-wideband (UWB) channel estimation and line-of-sight (LOS) vs. non-line of sight (NLOS) classification based on the application of the Unscented Kalman Filter (UKF) and the analysis of multipath channel response characteristics. For non-linear models, the UKF provides an efficient recursive minimum mean squared error estimation technique that is successfully applied in this work, and supported by numerical results demonstrating its effectiveness in UWB multipath channel gain and time delay estimation. The multipath channel response obtained from a limited number of channel taps is subsequently characterized in terms of relevant statistical parameters including Kurtosis and Mean Excess Delay. This characterization reveals clear differences in the statistics of these parameters under LOS and NLOS propagation conditions for various channel types including residential, office, outdoor and industrial environments. Based on the estimated parameters’ probability density function (PDF) under LOS/NLOS hypotheses, a likelihood ratio test (LRT) for hypothesis classification is performed for the different UWB channel models. Numerical results show that highly reliable LOS vs. NLOS classification is achievable with accuracy exceeding 90% for most cases of practical interest, which can then be further exploited in enhancing the performance of UWB-based positioning applications.

Keywords:

Channel estimation LOS/NLOS classification unscented Kalman filter UWB