Self-Learning Detection and Mitigation of Non-Line-of-Sight Measurements in Ultra-Wideband Localization

2021 ◽  
Author(s):  
Laura Flueratoru ◽  
Elena Simona Lohan ◽  
Dragos Niculescu
Keyword(s):  
Ultra Wideband ◽  
Line Of Sight ◽  
Self Learning ◽  
Non Line Of Sight

scholarly journals Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1238
Author(s):  
Javier San Martín ◽  
Ainhoa Cortés ◽  
Leticia Zamora-Cadenas ◽  
Bo Joel Svensson
Keyword(s):  
Autonomous Vehicles ◽  
Research Work ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Use Case ◽  
Positioning System ◽  
Precise Positioning ◽  
Research Institutes ◽  
Simulated Environment ◽  
Non Line Of Sight

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

scholarly journals NLOS Identification and Mitigation Using Low-Cost UWB Devices

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3464 ◽  
Author(s):  
Valentín Barral ◽  
Carlos J. Escudero ◽  
José A. García-Naya ◽  
Roberto Maneiro-Catoira
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Distance Measurements ◽  
Indoor Location ◽  
Indoor Scenarios ◽  
Indoor Location Systems ◽  
Clear Line ◽  
Non Line Of Sight

Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.

Ultra Wideband Indoor Positioning Using Kalman Filters

2012 ◽  
Vol 433-440 ◽  
pp. 4207-4213 ◽  
Author(s):  
Li Zhang ◽  
Hao Zhang ◽  
Xue Rong Cui ◽  
T Aaron Gulliver
Keyword(s):  
Kalman Filter ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Time Difference Of Arrival ◽  
Positioning Systems ◽  
Positioning Technique ◽  
Performance Results ◽  
Source Of Error ◽  
Mitigation Technique ◽  
Non Line Of Sight

A time-difference-of-arrival (TDOA) positioning technique for indoor ultra wideband (UWB) systems is presented. Non-line-of-sight (NLOS) propagation error is a major source of error in positioning systems. Therefore an NLOS mitigation technique employing a Kalman filter is utilized to reduce the NLOS errors in indoor UWB environments. An extended Kalman filter (EKF) is used to process the TDOA data for mobile positioning and tracking. Performance results are presented which show that the proposed scheme can significantly improve the positioning accuracy in a UWB environment.

Non-Line-of-Sight Identification for Indoor Positioning Using Ultra-WideBand Radio Signals

Navigation ◽  
2013 ◽  
Vol 60 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Junlin Yan ◽  
Christian C. J. M. Tiberius ◽  
Giovanni Bellusci ◽  
Gerard J. M. Janssen
Keyword(s):  
Indoor Positioning ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Radio Signals ◽  
Wideband Radio ◽  
Non Line Of Sight

scholarly journals Non-parametric Error Modeling for Ultra-wideband Localization Networks

2019 ◽  
Author(s):  
Acshi Haggenmiller ◽  
Maximilian Krogius ◽  
Edwin Olson
Keyword(s):  
Error Modeling ◽  
Parametric Estimation ◽  
Ultra Wideband ◽  
Estimation Methods ◽  
Line Of Sight ◽  
Probability Densities ◽  
Non Parametric Estimation ◽  
Non Line Of Sight ◽  
Non Parametric

ICRA 2019 Paper Submission Code and DatasetsWe propose an ultra-wideband-based (UWB) localization system that achieves high accuracy through non-parametric estimation of measurement probability densities and explicit modeling of antenna delays. This problem is difficult because non-line-of-sight conditions give rise to multimodal errors, which make linear estimation methods ineffective. The primary contribution in this paper is an approach for both characterizing these errors in situ and an optimization framework that recovers both positions and antenna delays. We evaluate our system with a network of 8 nodes based on the DecaWave DWM1000 and achieve accuracies from 3 cm RMSE in line-of-sight conditions to 30 cm RMSE in non-line-of-sight conditions. Collecting measurements and localizing the network in this manner requires less than a minute, after which the realized network may be used for dynamic real-time tracking.

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