# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 1 Introduction  
Location-based services in the fifth generation (5G) mobile network require reliable, continuous, and precise positioning information for their full functionality potential [1]. Global navigation satellite systems (GNSS) have settled as the reference localisation system for outdoor navigation. GNSS offer a meter-level accuracy at open sky scenarios. However, the precision is reduced drastically when the target enters a building or tunnel. Several technologies (e.g., WiFi and Bluetooth) and techniques (e.g., fingerprinting and image recognition) try to provide accurate and precise location information [2, 3]. Nevertheless, indoor scenarios are extremely challenging due to the harsh radio propagation conditions. Indoor scenarios usually contain metallic objects that reflect and block the signals creating multipath effects that can strongly deteriorate the navigation solution or create areas where no navigation information is available. Moreover, typical indoor scenarios are dynamic with constant changes due to the mobility of people within the scenario, such as in a shopping mall or an office. High-precision positioning becomes crucial for some Internet of Things (IoT) services, such as augmented reality (AR) or context-aware applications. In recent years, some technologies have emerged for precise indoor localisation. There are two main families of techniques: based on trilateration, and based on fingerprinting. Trilateration consists in obtaining the position of the target based on the intersection of the distance between the target and at least three reference points. Some technologies that are being studied are ultra-wide band (UWB) [4], WiFi fine-time-measurement (FTM) [5]and cellular-based radio [1]. UWB has been widely adopted due to its robustness against multipath and its centimetre-level accuracy [6]. It may become the standard for indoor positioning in the next years as defined by ETSI [7]. Since UWB devices have a range of up to few tens of metres, they need a fairly dense deployment to ensure the required coverage. However, a dense deployment of UWB in the real world has a very high cost, making it feasible only for limited scenarios. WiFi FTM relies on the wide availability of WiFi access points (APs), and the higher range of WiFi signals to reduce the deployment costs, but it is still a fairly new technology that has not a wide commercial adoption. Although 5G ranging is still under research [8], it promises a very high availability thanks to the omnipresence of 5G base stations. Long term evolution (LTE) has also been used for obtaining location, although its precision is not as high as UWB or WiFi FTM [9]. WiFi fingerprinting [2; 3] has also been widely studied. In fingerprinting, instead of using reference points, the terrain is divided in a lattice, and for each division, the set of visible WiFi APs is collected in an offline stage. For estimating location, a target will then obtain a list with the visible APs, and will use it to find the point in the lattice where it is most likely located. The map must be frequently updated to reflect changes in the environment. In this work, we propose a method for opportunistically aggregating ranges obtained from different technologies. This fusion technique helps to reduce the cost of deployment because the end-user benefits from any nearby ranging reference point (RP) for localisation [10]. In addition, this technique also helps dealing with coverage holes of certain deployments, that is, areas where there are less than three visible reference points of one technology. Since not all ranges have the same precision, we propose a weighting stage that prioritises the reference points that offer a better location quality. To this end, this work uses a maximum likelihood estimator (MLE) to characterise the ranges and sources in order to define the weighting algorithm to balance the information of the over-determined system which provides high accuracy indoor positioning.