# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Fingerprinting Localization Algorithm  
In IPS and indoor location-based services, the fingerprinting (FP) localization method is a prevalent method to attempt the optimizing of position accuracy using range-free information in building structures, for example, in shopping malls, convenience shops, market places, offices, hospitals, airports, factories, industries, campus buildings and smart buildings. To solve the difficulties in IPS, the FP localization algorithm has the ability to obtain high positioning accuracy, reducing the hardware complexity and undesirable influence of the multipath effect, better than range-based methods. Figure 11: Maximum likelihood-based positioning method. The fingerprinting method is normally formulated in two phases, the offline phase (training) and online phase (testing). The basic operation of this method is as shown in Figure 12.  
In the offline phase, the spatial-temporal RSS data from each AP location are gathered and saved in the database as current location coordinates, called RPs. Moreover, the database of previously known patterns received from a known Wi-Fi base is collected by uniformly selecting RSS measurements for each point as an FP. In the online phase, the mobile device or receiver accepts the new RSS measurements from different APs. Then, the comparison and recognition processes are performed between the measured RSS values and reference FPs for position estimations. An FP localization technique brings new challenges that the primarily fingerprints database should be accurate for the requirement of good performance and desired positioning accuracy [176]. An FP method could find the target's position by utilizing RSSI measurements that come from various transmitters or different network sources. Many different APs' location diminish the positioning accuracy due to RSS noise and attenuation. RSSI works in MAC which is known as the datalink's sublayer in the OSI model, which is the available wireless network interface controllers by access points (APs). In the MAC layer, a radio map can build itself by using the signal strength of APs which is known through offline processing. Especially, wireless-based positioning, and RSSI measurements, are stored in the database and matching between the information of stored data and the current target position of the RSSI radio map measurements. Thus, the time consumption and labour-intensive aspects take part as essential issues to construct the radio map for collecting data [176; 177; 178]. In addition, RSSI FP localization are commonly applied; it is cost effective and relieves the complexity of additional hardware for an indoor positioning system. According to the indoor environment, the complexity and the estimation of performance methods are considered by the implementation of the Wi-Fi deployment stage. The relative strength of the known Wi-Fi-based stage is used to address the accurate position as well as the propagation model of a known antenna is used to predict the distance by the signal propagation of time-based methods, such as triangulation [179], trilateration [1; 161; 162] and multilateration [63; 164]. However, the conventional time-based method is not sufficient for the RSS ranging aspect of indoor performance. Generally, the FP based on radio maps can be divided into deterministic and probabilistic approaches. Deterministic and probabilistic approaches are utilized for measurements of indoor positioning using RSSI [179; 180; 181; 182; 183; 184; 185; 186; 187], as shown in Figure 13.  
Figure 12: Fingerprinting-based positioning method. The deterministic approach is based on the fixed values of known variables; it only takes certain variable values without the consideration of uncertain random variables. Indeed, a deterministic algorithm is finding the optimization of similarity between the new measurements of online data and the dataset of FPs offline.