## 1 Introduction
### Hybrid Localization Systems  
In hybrid localization systems, user localization is computed based on different types of measurement data obtained using one or more various technologies. The first group of hybrid localization systems are systems in which the user is localized using one technology based on various signal factors. Most notable examples combine typical measurements of RSS or TDOA with direction estimation AoA. In [21], a simple single device localization system is presented. The device uses RSS measurements to estimate a distance from the localized object and AoA to estimate direction, in which it is located. Such approach allows to minimize the number of reference anchor nodes to one. An interesting example of AoA usage in hybrid localization schemes is presented in [22], where a hybrid TDOA/AOA Extended Kalman Filter based algorithm for UWB localization system is described and tested. Another hybrid combination is RSS/TDOA, which can be used in WiFi-based systems [23]. In the second group of hybrid localization solutions, the users are localized using two or more different technologies. A very popular combination is fusing results obtained using the radio part with data from inertial measurement units. In [24], smartphone results from inertial sensors and magnetometer are used to support BLE-based fingerprinting. The presented solution allowed to localize the robot in places, where there was no coverage of BLE anchors, and quality of the radio map was poor. In [25], IMU senors were used for movement direction and gate analysis. The additional data allowed to significantly improve BLE positioning accuracy. A similar approach was taken in [26], where a single accelerometer was used for gait analysis, which allowed to improve localization accuracy in a UWB system. Another large group is that of hybrid systems mixing two or more radio technologies. In [27], an asset tracking system using WiFi and Bluetooth is presented. It uses WiFi fingerprinting for early, rough asset localization and when the user gets closer, localizes it with greater accuracy using BLE trilateration. The same combination was used in a system deployed in a university library [28]. Here, the users were localized using BLE and WiFi fingerprinting. The system utilizes the fact that due to different anchor placement and propagation conditions, accuracy of the radio maps for BLE and WiFi varies in the same places. The localization errors for both maps are estimated during the map creation. During normal system operation the users are localized using both technologies concurrently and the more probable result is chosen. Another combination of technologies is BLE-UWB. In [29], UWB was used periodically to improve BLE-based localization. In [30], it was used in the process of radio map creation, which consisted in gathering points for the BLE radio map while localizing the user using UWB and then interpolating the missing parts based on the fitted exponential propagation model. Solutions combining radio technologies with other types of sensors are much less common. In [31], a system concept, where the users are roughly localized using BLE and then tracked with vision systems is presented. Another example is [32], in which WiFi and ultrasound-based hybrid fingerprinting is presented. An interesting type of sensors, which is not that popular and could be used in hybrid localization, are proximity and ranging sensors. They are usually used in robotics for SLAM (Simultaneous Localization and Mapping) but some examples of their use for localization can also be found. In [33], the sensors are used to form an array monitoring a small area of 600 \(\times\) 300 mm dimensions. The sensors are used to track a round object moving around on a robot. The localization errors are very low and below 1 cm. Simulation of another example of a proximity sensor network is presented in [34]. The described proximity or ranging sensors network solutions would allow for easy device-free localization.