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, 05/17/2022 - 22:21
DOI:
10.21227/rhhs-fw33
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Link to Paper:
License:
Creative Commons Attribution
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Abstract 

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: 

Please, if you use this dataset in your research activities, please add a reference to our related paper:

 

Barral, V.; Escudero, C.J.; García-Naya, J.A.; Suárez-Casal, P. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems. Sensors 2019, 19, 5438. https://doi.org/10.3390/s19245438

 

 

 

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

Dataset Files

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