Abstract
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.
Highlights
In recent years, indoor localization technique has received a lot of attention
After the off-line phase, the indoor localization system based on deep belief network (DBN) model is established and used to predict the position of a Blind Node (BLN) according to the impulse sequence received by the Beacon Node (BN)
The fingerprinting databases used in DBN, k-nearest neighbor (kNN), SVR, and neural networks (NNs) are the same
Summary
Indoor localization technique has received a lot of attention. Some concepts of outdoor location systems are used in indoor localization, but they are always subject to big position errors. Compared to the geometric localization technique which is widely used in outdoor localization tasks, fingerprinting localization technique provides a different method to determine the position of a target in an indoor environment [2,3,4]. It can be divided into two phrases: the off-line phase and the online phase. DBN is an effective machine learning model proposed in 2006 [11] It has been widely used in many different fields such as image [12], speech [13], and language processing [14].
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