Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques

• Rodríguez-Damián, María ^[1] ; Vila, Xosé A. ^[1] ; Rodríguez-Liñares, Leandro ^[1]
  1. [1] Universidade de Vigo

     Universidade de Vigo

     Vigo, España

     
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 19, Nº. 1, 2019 (Ejemplar dedicado a: Forty-Nineth; e08), págs. 1-7
• Idioma: inglés
• DOI: 10.24215/16666038.19.e01
• Enlaces
  • Texto completo
• Resumen
  • español

    Actualmente en el campo de la localización de objetos en interiores está recibiendo mucha atención por parte de los investigadores, pero todavía necesita de la madurez suficiente para su integración en dispositivos tan populares como los teléfonos móviles. En este artículo se describen los resultados de un experimento realizado para comparar diferentes algoritmos de reconocimiento de patrones con el fin de procesar la información de un conjunto de transmisores Bluetooth, situados en posiciones fijas, con el objetivo de localizar un objeto en una posición precisa. Nuestra conclusión es que los mejores algoritmos, entre los cinco que hemos experimentado son: Random forest y model-based clustering, que alcanzaron una precisión cercana al 90%. También hemos llevado a cabo experimentos para analizar la influencia del número de transmisores Bluetooth y para determinar los conjuntos de características con mejor rendimiento. El enfoque propuesto es simple y proporciona un 90% de precisión para la localización de objetos con una precisión de 1 m, lo que lo hace adecuado para una amplia gama de aplicaciones.

  • English

    Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.

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