In:
Sensors, MDPI AG, Vol. 19, No. 24 ( 2019-12-10), p. 5438-
Abstract:
Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.
Type of Medium:
Online Resource
ISSN:
1424-8220
Language:
English
Publisher:
MDPI AG
Publication Date:
2019
detail.hit.zdb_id:
2052857-7