3 research outputs found
Statistical LOS/NLOS Classification for UWB Channels
Ultrawideband (UWB) technology has attracted a lot of attention for indoor
and outdoor positioning systems due to its high accuracy and robustness in
non-line-of-sight (NLOS) environments. However, UWB signals are affected by
multipath propagation which causes errors in localization. To overcome this
problem, researchers have proposed various techniques for NLOS identification
and mitigation. One of the approaches is statistical LOS/NLOS classification,
which uses statistical parameters of the received signal to distinguish between
LOS and NLOS channels. In this paper, we formulated several techniques which
can be used for effectively classifying a Line of Sight (LOS) channel from a
Non-Line of Sight (NLOS) channel. Various parameters obtained from Channel
Impulse Response (CIR) like Skewness, Kurtosis, Root Mean Squared Delay Spread
(RDS), Mean Excess Delay (MED), Energy, Energy Ratio, and Mean of Covariance
Matrix are used for channel classification. In addition to this, the Joint
Probability Density Functions (PDFs) of various parameters are used to improve
the accuracy of UWB LOS/NLOS channel classification. Two different
criteria-Likelihood Ratio and Hypothesis Tests are used for the identification
of the channel
A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model
In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy