## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems
#### 3.6.1 RSSI-IMU Hybrid Systems  
Many authors have experimented with the combination of Wi-Fi and inertial location. Studies have searched for better arrangements between the two models so that one will reduce the limitations of the other. A weakness reported in these studies is that pure inertial systems are weak because errors in estimation accumulate progressively, and pure Wi-Fi location systems are not accurate and responsive to user movements. Hybrid RSSI-IMU systems include methods that combine RSSI measurements with inertial measurements, using either a propagation model or a fingerprinting approach [95]. The availability of Wi-Fi networks deployed in millions of buildings worldwide makes RSSI-based positioning systems an attractive hybrid option because there is no need to invest in wireless infrastructure. One of the countless advantages of using Wi-Fi networks is the ability to calculate RSSI just by listening to the network, as most wireless communication standards already include the RSSI field in radio packages. A hybrid strategy associates Wi-Fi signals to magnetic fields through the application of a particle filter and a Kalman filter [117]. In these studies, the objective was to create a collaborative scheme in which one technology compensated for the deficiency of the other [117]. Thus, due to the differences in Wi-Fi signals being greater than those perceived in magnetic fields, but providing a physical reference, some authors use Wi-Fi to indicate an area, and internally to this area, inertial information reinforces the perception of a location. [PERSON] et al. described an example of this type of hybrid system, in which the combination of visible light communication (VLC) with the inertial measurement unit (IMU) estimates the user's position on the scenario map [118]. The authors applied a linearization method to determine quaternary and velocity errors with adaptive Kalman filter (AKF). An indoor positioning algorithm using the unweighted Kalman filter was developed to obtain the final positioning solution. The results show that the proposed integrated VLC and IMU algorithm reduced the errors of the positioning solution to less than 0.23 m. [PERSON] et al. also combined the inertial system with RFID tags, which provide RSSI values, via Extended Kalman filter (EKF) [119]. [PERSON], [PERSON], and [PERSON] described other examples of hybrid systems [9; 45; 120]. These authors configured the IMU sensor at the level of the user's foot or hip and combined the inertial information with the position estimates of an RSSI-based system. A concern in many studies is how to reduce the oscillations of signals obtained by inertial sensors caused by the environment itself and signals from other sensors [85]. Several authors have applied different strategies to improve the accuracy of the measurement to smooth out the noise with the use of filters. [PERSON] and [PERSON] applied particle filters and Monte Carlo filters on inertial data to stabilize the values to be submitted to the location algorithm [59; 85]. The authors reported an accuracy of about 1.50 m for the mapped reference place, and this value was much lower than the approximately 6.00 m using only fingerprints on Wi-Fi signals. [PERSON] et al. and [PERSON] et al. also presented a system that combines Wi-Fi and inertial location technologies [9; 45]. Wi-Fi location is estimated using fingerprinting from signal strength measurements, and the inertial part identifies the user's displacement and direction of travel by using the accelerometer and gyroscope sensors. The authors justified the use of fused Wi-Fi and inertial systems as a strategy to reduce the limitations of each component. Location by Wi-Fi signals cannot identify users' movements, while inertial systems accumulate errors in the estimation of position over time. The data were combined using the Monte Carlo method, which applies a particle filter to eliminate the most dispersed records [121]. The results obtained in the study indicate an accuracy of 1.53 m in a 40.00 m walk.