# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 2 Related Work  
Several works have proposed learning machines as good candidates to estimate the tag position from RSS measurements. In [24], a kind of artificial neural network, called multilayer perceptron (MLP), was proposed to map for the first time the relation between coordinates (\(X\),\(Y\)) of the tag position and the RSS gathered by an _ad hoc_ WLAN. In [23; 24; 25; 26; 27], other types of learning machines, such as support vector machines (SVM), learning vector quantization (LVQ) and radial basis functions (RBF), were respectively studied for the same purpose, obtaining similar results. Up to date, most of these systems offer a coarse precision of 3-6 m for 90% of estimations [4], if no additional information/sensors are integrated [16]. This precision can be acceptable for many location-based services (LBS), which require room-/area-level localization [13; 21; 22; 23], but not for exact point location. In fact, room-level accuracy has high interest given the number of highly practical applications that can be integrated with low cost techniques and quite extended commercial systems (smartphones, tablets, _etc._). However, there is still a problem with service reliability, because of the RSS temporal fluctuations. At this point, it is good to note that the presented work is in the frame of the Spanish Project \"ChAracterization, EvaLuation, Planning and IMprovement of Key Technologies for the Future Internet: Knowledge and Transfer\" (CALM), Project Ref. TEC2010-21405-C02-02, which was intended to provide room-level localization of patients inside a hospital. In many particular applications as this one, meter/submeter indoor positioning is not necessary, and less accurate, much more inexpensive room-sized localization is adequate. This type of room-level solution can be found in many commercial products; see for instance [39; 40; 41; 42; 43]. Several previous works have proposed different approaches to provide room-level positioning so far, which seem to have obtained good results. In [13], a coarse room-level accuracy is given based on selecting the anchor node with the maximum associated RSS. Each anchor is associated with a room. However, problems arise when the tag is close to walls. This issue is improved in [21], where a path-restricted room-level localization system is proposed with additional intelligence, achieving an average success probability of 94%. Furthermore, high success is achieved in [22; 23], where RSS-based probabilistic algorithms are applied to refine the room-level tag location based on detecting tag distance to passable boundary points (e.g., at entrances) [22] or calculating a transition probability between points [23], so they detect when the tag is likely moving between predefined areas with additional post-processing techniques. According to the experiments, around 95% of success is the average in [22] and 97% in [23], respectively, in controlled conditions. However, all previous cited works assumed omnidirectional antennas for the sensor readers and tags, obviating the interesting properties of non-isotropic (directional) antennas. Unlike omnidirectional antennas, directional antennas may report better spatial discrimination, because of their higher directivity at some angular directions. Some works have proposed exploiting directional antenna properties in a different way [44; 45; 46; 47; 48; 49; 50; 51; 52]. In [44], a review of antennas applied for indoor localization systems can be found. An AOA-based technique was presented in [45], where the angle was estimated from the difference between RSS captured by two directional antennas. Furthermore, [46; 47; 48; 49; 50; 51; 52] present similar AOA-based techniques employing directional antennas for location estimation in wireless sensor networks (WSN), reducing to two the minimum number of anchor nodes (sensor readers (SRs)) to estimate the tag position by multilateration techniques.