Title :
HIWL: An Unsupervised Learning Algorithm for Indoor Wireless Localization
Author :
Li Li ; Wang Yang ; Guojun Wang
Author_Institution :
Sch. of Inf. & Eng., Central South Univ., Changsha, China
Abstract :
An advanced unsupervised learning algorithm for a precise measurement of the local position of an indoor mobile target is proposed. In this work, the indoor wireless localization is addressed with HIWL, an unsupervised learning algorithm based on HMM. The locations of reference nodes and site survey are no longer needed in this algorithm. A sample data process with K-means which helps us produce discrete observation sequences is introduced. Also, a family of equations to compute effective initial parameters of HMM is presented. Experiments show that HIWL can achieve better localization accuracy.
Keywords :
hidden Markov models; learning (artificial intelligence); mobile computing; position measurement; HIWL; HMM; K-means; discrete observation sequences; indoor mobile target; indoor wireless localization; local position measurement; sample data process; unsupervised learning algorithm; Accuracy; Hidden Markov models; Mobile handsets; Unsupervised learning; Vectors; Wireless communication; Wireless sensor networks; HMM; unsupervised learning; wireless localization;
Conference_Titel :
Trust, Security and Privacy in Computing and Communications (TrustCom), 2013 12th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/TrustCom.2013.217
