# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 1 Introduction  
Many context-aware applications require services dependent on the user/tag location [1]. To name a few, guide navigation in museums and malls or asset tracking for logistical purposes in hospitals, airports or general industrial environments are some of the most promising services currently demanded. Most of these applications usually require indoor location, where global navigation satellite systems (GNSS) cannot guarantee location service due to the obstruction of the satellites' signals [2]. For this reason, alternative indoor localization systems (ILS) employing different technologies are increasingly drawing more attention, [3]. Most of them are differentiated by the type of metrics employed to infer the position, such as time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) or received signal strength (RSS). Systems that incorporate TOA, TDOA or AOA measurements usually achieve high location accuracy with errors below \(1\) m, but at the expense of more complex and expensive devices than RSS solutions due to the need for complex synchronization schemes and higher bandwidth requirements [4; 5; 6; 7; 8]. Currently, solutions based on RSS are quite attractive because of their easy integration in wireless networks and extremely low cost, since RSS measurement is usually reported by most of the current standards [9]. Basically, in the literature, three different techniques can be found to estimate the position from the RSS. (1) Proximity algorithms [10] provide symbolic relative location information. In these cases, the tag position is associated with the known location of the sensor that measures the strongest RSS value. They usually rely on a dense grid of sensors; (2) Distance-basedmethods [9; 11; 12; 13; 14] require estimating the distance between the tag and the sensors from the RSS, obtaining the tag location by multilateration algorithms [9]. However, a good radio propagation model of the indoor electromagnetic channel is required [12; 13; 14; 15], so the greater precision of the model, the better location accuracy achieved. These models are not trivial and usually require a custom-made survey of the indoor environment under consideration to obtain the model parameters. Finally; (3) the third group of systems encompasses those ones based on the fingerprinting-based technique (also called scene analysis) [16; 17; 18; 19]. Fingerprinting has become one of the most popular and extended techniques applied to RSS-based indoor positioning systems. This technique mandatorily requires a site survey of the particular scenario of interest in order to gather a set of fingerprints composed of RSS vectors associated with the possible space locations where the tag can be positioned. Thus, a fingerprint database is created, which is generally called a radio map. Several approaches based on different architectures and positioning algorithms [4] have been proposed to estimate the tag position from the radio map. Among others, the most outstanding algorithms are maximum likelihood (ML) [20; 21; 22], K-nearest neighbors (KNN), weighted KNN (WKNN) [11] and diverse learning machines, such as support vector machines (SVMs) [23; 24], radial basis functions (RBF) or artificial neural networks (ANN) [25; 26; 27; 28; 29]. ML, KNN or WKNN consist of comparing gathered RSS vectors with the RSS fingerprints stored in the radio map. Thereby, the most likely tag position is figured out. On the other hand, machine learning algorithms need an intermediate training phase after the site survey and before the operating stage. The machines must be previously trained so they can learn the mapping function between RSS signal space vectors and spatial locations in the radio map. After the training process, these learning machines will act as classifiers, providing the most probable location according to the knowledge acquired.