- Title
- Dynamic indoor localization using maximum likelihood particle filtering
- Creator
- Wang, Wenxu; Marelli, Damián; Fu, Minyue
- Relation
- Sensors Vol. 21, Issue 4, no. 1090
- Publisher Link
- http://dx.doi.org/10.3390/s21041090
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2021
- Description
- A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.
- Subject
- indoor tracking; particle filter; channel state information; WiFi fingerprinting
- Identifier
- http://hdl.handle.net/1959.13/1425744
- Identifier
- uon:38303
- Identifier
- ISSN:1424-8220
- Rights
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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