Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems

Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems

Citation Author(s):
Valentín
Barral
University of A Coruña
Carlos J.
Escudero
University of A Coruña
José A.
García-Naya
University of A Coruña
Pedro
Suárez-Casal
University of A Coruña
Submitted by:
Valentin Barral
Last updated:
Tue, 09/03/2019 - 17:02
DOI:
10.21227/rhhs-fw33
Data Format:
License:
Creative Commons Attribution
Dataset Views:
151
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Indoor positioning systems based on radio frequency systems such as UWB inherently present multipath related phenomena. This causes ranging systems such as UWB}to lose accuracy by detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects. For this purpose, real cross scenarios are considered, where the data extracted from UWB low-cost devices for the training of the algorithms come from different environments than those considered for the real application and its analysis.

Instructions: 

These are Matlab files.

 

The measurements were recorded in the scenario shown in the figure.

Three configurations where used:

- "h0_front" contains the measurements with the tag facing North at the same height than the anchors.Height = 1.28m.

- "h0_back.mat " includes the measurements  with the tag facing South at the same height than the anchors. Height = 1.28m.

- "h1_front" includes the measurements with the tag facing North at a higher altitude  than the anchors. Tag Height = 2.05m. Anchors Height = 1.28m.

 

How to use:

 

In Matlab:

 

 

 

h0_front = load('h0_front.mat');

h0_back = load('h0_back.mat');

 

h1_front = load('h1_front.mat');

 

 

where

"h0_front" contains the measurements with the tag facing North at the same height than the anchors.

"h0_back.mat " includes the measurements  with the tag facing South at the same height than the anchors.

 "h1_front" includes the measurements with the tag facing North at a higher altitude  than the anchors.

 

 

The file contains 3 arrays:

- beacons (1x5 struct) Contains the coordinates of each of the 5 anchors.

- pos (1x9 struct) Contains the coordinates of the 9 measurement points.

 

- ranging (1x9 struct) Each row contains the measurements from one of the 9 positions of the array. The struct  includes:

 

 

- range (Nx1) int64. The value outputted by the device. In cm.

 

- rxPower (Nx1) double. The received power strength. In dBm.

 

- timestamp (Nx1) double. Measurement timestamp. In unix time.

 

- angle (Nx1) double. Not used in this set.

- destinationId (Nx1) int64. The index of the anchor

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[1] , "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/rhhs-fw33. Accessed: Sep. 18, 2019.
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doi = {10.21227/rhhs-fw33},
url = {http://dx.doi.org/10.21227/rhhs-fw33},
author = { },
publisher = {IEEE Dataport},
title = {Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems},
year = {2019} }
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T1 - Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems
AU -
PY - 2019
PB - IEEE Dataport
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ER -
. (2019). Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems. IEEE Dataport. http://dx.doi.org/10.21227/rhhs-fw33
, 2019. Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems. Available at: http://dx.doi.org/10.21227/rhhs-fw33.
. (2019). "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems." Web.
1. . Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/rhhs-fw33
. "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems." doi: 10.21227/rhhs-fw33