NLOS identification and compensation for UWB ranging based on obstruction classification

Abstract

Abstract-Non-line-of-sight (NLOS) propagation is one of the major barriers to accurate ranging and positioning based on time of arrival (TOA) in the application of an ultra wideband (UWB) system. This paper proposes a new method for NLOS identification and mitigation based on signal characteristic analysis and fuzzy theory. This method neither requires to build a statistical model nor to create and update a training database, so that it can be used conveniently for different application scenarios. Extensive experiments were conducted and the results show that the cumulative distribution function of the ranging error below 0.5 meter is over 90% when using the proposed mitigation method, while that without using the mitigation method is below 70%. Also, by using the proposed method, the root mean square error (RMSE) of the range measurements is reduced from 0.77 to 0.33 meter. The results demonstrate that this method can effectively identify NLOS and mitigate the NLOS-induced ranging error.

An Adaptive Approach for Ultra-Wideband Positioning in Complex Environment

“…The Gaussian process is a desirable tool to solve regression problems [25]. The GPR model can be generally described as: = ( ) yf+ x (16) where, x is the input vector; f is the function value corresponding to the input;…”

“…The indirect mitigation methods end up by estimating the tag location through mature LOS positioning strategies [11][12][13][14][15][16]. A typical indirect mitigation method is the hybrid method [17], which boasts a high positioning accuracy under the coexistence of line-of-sight (LOS) and the NLOS signals.…”

“…To achieve accurate estimation through regression, it is necessary to divide the NLOS signal samples into mild and severe obstruction propagation signals according to the magnitude of their ranging errors. Recently, Wen et al [16] developed an NLOS discrimination and compensation method based on obstruction classification. The method can achieve good positioning accuracy, only if the received signal propagates through one class of obstruction.…”

Bias Compensation for UWB Ranging for Pedestrian Geolocation Applications

“…However, dropping NLoS measurements limits the effectiveness of the UWB measurement feedbacks in complex indoor environments. Another approach to deal with bias in the NLoS UWB ranging measurements is to use machine learning methods to identify and remove the bias [7]- [11]. For example, [11] uses the classification of obstruction material based on analyzing the statistics of Channel Impulse Response (CIR) to estimate and remove the bias.…”

“…Another approach to deal with bias in the NLoS UWB ranging measurements is to use machine learning methods to identify and remove the bias [7]- [11]. For example, [11] uses the classification of obstruction material based on analyzing the statistics of Channel Impulse Response (CIR) to estimate and remove the bias. We used the method of [11] to experimentally test the effectiveness of the classification methods for bias removal in a series of experiments with different obstruction materials.…”