# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Fingerprinting  
Fingerprinting is one of the most commonly used ML localization techniques in the literature [60]. It mainly relies on the analysis of specific measurements or features of the signal received by the unknown receiving node. It consists of the offline data collection phase and the online matching phase, as shown in Figure 9.  
Figure 9: Fingerprinting localization technique, reproduced with permission from [PERSON], _Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study_, published by Indonesian Journal of Electrical Engineering and Computer Science, 2016. During the offline phase, a site survey is carried out consisting of the unique location fingerprints, i.e., signal characteristics, coordinates of sampling points, and signal strength [22]. It can be classified as either deterministic or probabilistic [24]. The deterministic approach involves the storing of different fingerprints, whereas the probabilistic computes some statistic of the measurements first, such as taking a simple average of the RSS measurements. The deterministic requires less calibration effort, while the probabilistic requires the computation of the statistics for each calibration point [8]. Furthermore, during the online phase, real-time measurements are collected and compared with the already stored ones from the offline phase. This comparison occurs during the matching phase and is commonly based on a matching score [30], thus finding the best match between the stored and real-time fingerprints, which involves simple matching techniques, i.e., distance estimations and cross correlation metrics [24; 27]. The \(k\)-NN is one of the most commonly used ML algorithms for performing the matching. Fingerprinting approaches for localization, however, require large sets of training data, which are sometimes difficult to collect. In particular, the training and computational complexity requirements remain significant challenges, especially when they occur on low computational power devices. The usage of sensor fusion on the server side can assist, but at the expense of increased latency, which is a problem for real-time positioning systems [24]. Moreover, in the 2019 IPIN competition, some competitors argued that the fingerprinting techniques are not a viable solution for future indoor localization systems [59]. The system's scalability is another ML challenge, as well as the time-consuming procedures for the collection and comparison process. The comparing process time can be reduced by dividing the database into numerous clusters and comparing data from the target node only with the data in the corresponding cluster [27]. Similarly, the Support Vector Machines (SVMs) algorithms can be used for increasing the flexibility of the ML. In particular, they can adopt kernel mechanisms for finding the difference between two points of two separated classes, as well as modeling the linear and non-linear relations with better generalization performance. In addition, they can utilize the different channel characteristics and identify the LoS and NLoS classes [9]. Still, SVMs are characterised as time-consuming algorithms with high memory requirements, especially when the number of support vectors becomes large [27]. For instance, the \"RADAR\" [41] pioneered the fingerprinting by utilizing the Wi-Fi technology, and since then, it has been a popular localization technique, with RSS measurements collected from various Wi-Fi access points (APs). The RSS can be used in combination with CSI, since the main path of the received signal can be easily distinguished from the reflected signals, and CSI provides more information, compared to RSS fingerprinting [40]. Fingerprinting complexity and costs are significantly low, since no additional hardware deployment and time synchronization between the nodes are required. Although it is a simple, easily adaptable and inexpensive technique, its updating and maintenance are challenging, due to its sensitivity to the environment changes [24; 27].