Cargando…

Modeling Infrared Signal Reflections to Characterize Indoor Multipath Propagation

In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR signals (IR-LPS) to evaluate t...

Descripción completa

Detalles Bibliográficos
Autores principales: De-La-Llana-Calvo, Álvaro, Lázaro-Galilea, José Luis, Gardel-Vicente, Alfredo, Rodríguez-Navarro, David, Bravo-Muñoz, Ignacio, Tsirigotis, Georgios, Iglesias-Miguel, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424724/
https://www.ncbi.nlm.nih.gov/pubmed/28406436
http://dx.doi.org/10.3390/s17040847
Descripción
Sumario:In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR signals (IR-LPS) to evaluate the behavior of transmitted signal Multipaths (MP), which are the main cause of error in IR-LPS, and makes several contributions to mitigation methods. Current methods are based on physics, optics, geometry and empirical methods, but these do not meet our requirements because of the need to apply several different restrictions and employ complex tools. We propose a simplified model based on only two reflection components, together with a method for determining the model parameters based on 12 empirical measurements that are easily performed in the real environment where the IR-LPS is being applied. Our experimental results show that the model provides a comprehensive solution to the real behavior of IR MP, yielding small errors when comparing real and modeled data (the mean error ranges from 1% to 4% depending on the environment surface materials). Other state-of-the-art methods yielded mean errors ranging from 15% to 40% in test measurements.