# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 1 Introduction  
Today, determination of indoor location and position of objects is an important subject, which provides many applications in various fields such as robotics, asset tracking, warehouse management, crowd analysis [1; 2; 3; 4]. The idea of indoor positioning and localization based on the strength of wireless signal was first introduced in 2000 [5]. Since then, it has sparked great interest and become the subject of many research areas. The concepts of positioning and localization are often confused. The position of an object corresponds to a specific point in a coordinate system such as longitude and latitude. However, the location of an object provides details of the position such as office, floor, building, etc. In this regard, localization can be defined as a simpler or categorical state of the positioning process. Localization is sufficient for applications that require less precision such as crowd analysis and storage-asset tracking. Positioning, on the other hand, is required for more precise operations such as robotics. Today, creating an economic, accurate, and precise system by utilizing the already installed Wi-Fi infrastructure is one of the most important goals of the research community. To this end, it is essential to use the pattern created by the propagation and address broadcasting of the Wi-Fi access points. The pattern provided by the numerous access points that are fixed to various points becomes unique by undergoing various reflections and weaknesses with the contribution of its spatial structure. This unique pattern is called Wi-Fi fingerprint. The simple Wi-Fi fingerprint data consist of the received signal strengthindicator (RSSI) and the media access control (MAC) address data. RSSI is a term used to measure the quality of a received signal of a client device. RSSI acts as a relative received signal strength because each chip manufacturer has its own receiver characteristics for the standard of IEEE 802.11. RSSI is simply an indication of the power level received by the receiver after the antenna and possible cable losses. Thus, the higher the RSSI value is, the stronger the signal is. If two RSSI values are represented as negative (for example, \(-100\) and \(-50\)), the one that is closer to \(0\) (\(-50\)) is the stronger received signal. However, when the RSSI information is analyzed with the spectrum analyzer, new fingerprint data called channel status information (CSI) are generated. In wireless communications, CSI refers to known channel properties of a communication link. It describes how a signal propagates from the transmitter to the receiver and represents the combined effect of scattering, fading, and power decay with distance. Short-term CSI describes instant or very short listening/viewing of an existing channel condition. Long-term CSI, on the other hand, describes a statistical characterization of the data obtained as a result of the channel being listened to for long enough to eliminate the influence of other systems in the environment and periodical factors such as overtime, season of the year, or other patterns. In CSI, 14 new features are included in the analysis only when the channel power information is used because there are 14 main channels in IEEE 802.11. Further, the obtained data are mainly affected by environmental effects and channel usage; therefore, time series analysis is required. It has been demonstrated in many studies that successful positioning operations are performed by considering these points [6; 7; 8; 9]. While examining the positioning operations with CSI, it is important to note that data coming from the spectrum analyzer have been used in the positioning process to achieve high performance. However, this feature is not supported by mobile devices because they are not fully applicable for it. Until today, many positioning systems based on the changes in the RSSI indicator obtained from Wi-Fi or Bluetooth signals have been developed [10; 11; 12; 13; 14].