Publicação
On the usage of machine learning techniques to improve position accuracy in visible light positioning systems
| Resumo: | This paper investigates the usage of machine learning algorithms, applied to the task of position estimation in visible light positioning systems. Traditional approaches relying in trilateration usually resort to the application of the least squares method to find the position estimate. The least squares method is very prone to outlier information present in the data set, which reduces the estimation accuracy. This paper presents a strategy based on clustering and outlier removal able to improve the estimation accuracy. Clustering is based on DBSCAN, an algorithm used to find structure in unstructured data. The tuning parameters for DBSCAN are optimized following a linear regression supervised learning step, where a set of training examples with known real position is used. Simulation results show a 35% gain improvement in accuracy achieved with a moderate complexity increase. The minimum estimation error for the case scenario under study was 0.2 mm, with an r.m.s. error of 35 mm. |
|---|---|
| Autores principais: | Gradim, André |
| Outros Autores: | Pedro Nicolau Fonseca; Alves, Luis Nero; Mohamed, Reem E. |
| Assunto: | Visible Light Communication Visible Light Positioning Received Signal Strength Mean Square Error Unsupervised Outlier Filter |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | This paper investigates the usage of machine learning algorithms, applied to the task of position estimation in visible light positioning systems. Traditional approaches relying in trilateration usually resort to the application of the least squares method to find the position estimate. The least squares method is very prone to outlier information present in the data set, which reduces the estimation accuracy. This paper presents a strategy based on clustering and outlier removal able to improve the estimation accuracy. Clustering is based on DBSCAN, an algorithm used to find structure in unstructured data. The tuning parameters for DBSCAN are optimized following a linear regression supervised learning step, where a set of training examples with known real position is used. Simulation results show a 35% gain improvement in accuracy achieved with a moderate complexity increase. The minimum estimation error for the case scenario under study was 0.2 mm, with an r.m.s. error of 35 mm. |
|---|