Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology

With the increasing requirement of localization services in indoor environment, indoor localization techniques have drawn a lot of attention. In recent years, fingerprinting localization techniques have been proved to be effective in indoor localization tasks. Due to the complexity and variability of indoor environment, some traditional geometric localization techniques based on time of arrival (TOA), received signal strength (RSS), or direction of arrival (DOA) may cause big position errors. Unlike common geometric localization methods, fingerprinting localization techniques estimate the position of target by creating a pattern matching model or regression model for the measurement. Therefore, a suitable learning model is the key of a fingerprinting location system. This paper presents a fingerprinting based localization technique using deep belief network (DBN) and ultrawideband (UWB) signals in an office environment. Some location-dependent parameters extracted from channel impulse response (CIR) are used as signatures to build the fingerprinting database. The construction of DBN which is based on the fingerprinting database is also discussed in this paper. Experiment results show that, with appropriate fingerprinting database and model structure, the location system can get desired accuracy.