# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 4 Hybrid Positioning Systems
### Smartphone Hybrid Systems  
The popularization of smartphones among the world converted the smartphone in the perfect device for positioning. Any IPS that can be implemented in a smartphone has the potential to be used by millions of people, granting access to the mass market without the need of investing in devices for positioning. For this reason the research efforts of authors working in the field of pedestrian positioning focused on the smartphone technology during the last years. Furthermore, the different technologies included in the nowadays smartphones allow to implement hybrid systems using a single device. Note that a smartphone usually includes WiFi, GSM, LTE and Bluetooth radios as well as an IMU among other technologies like GPS.  
Many different examples of IPS based on smartphones can be found in the literature. For example in [55] an IPS for underground public transport systems is developed based on the information about the routes and inertial sensors. Another example can be found in [56], where the authors use a Least Square Support Vector Machine (LS-SVM) for the classification of the smartphone position (hand, pocket, head, etc.) and then combine the inertial data with the measurements from a WiFi fingerprinting method using a Hidden Markov Model (HMM). Notwithstanding, typically the position of the smartphone is fixed by the designer of the system. In [63] the authors combine the inertial measurements with RSS and magnetic fingerprinting using an EKF. Other authors employ the SLAM approach in the smartphone combining the inertial measurements with WiFi and magnetic fingerprints [57; 58]. In [65] authors combine inertial sensors with a WiFi fingerprinting method and employ BLE beacons for the correction of the inertial drift. Similarly, in [66] authors combine inertial sensors with BLE and WiFi measurements employing and Extended Kalman Filter. More accurate results can be obtained if the map information is available. In [59] the authors also use a HMM for the fusion of the WiFi and inertial measurements and incorporate the map information. Similarly, [60; 61] combine the WiFi fingerprints with the inertial measurements using a particle filter and in [62] the fusion is done with a Kalman filter. Authors in [67] combine the inertial measurements of a smartphone with the RSS measurements of a smartphone and a smartwatch through an EKF. The performance of these systems is summarized in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{4}{c}{**Technologies**} & \multirow{2}{*}{**Fusion Method**} & \multirow{2}{*}{**Area (m\({}^{2}\))**} & \multicolumn{2}{c}{**Error**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Scalability**} \\ \cline{2-2} \cline{5-10}  & **Wifi** & **IMU** & **Magnetic** & **Map** & & & & & & **Type** & **Value (m)** & \\ \hline [PERSON] et al. [56] & Yes & Yes & No & No & HMM & Building & RMSE & 4.55 & Medium & Limited by calibration \\ [PERSON] et al. [57] & Yes & Yes & Yes & Yes & SLAM & 450 & 95 th & 2.7 & Medium & Limited by calibration and complexity \\ [PERSON] et al. [59] & Yes & Yes & No & Yes & HMM & Floor & RMSE & 3.1 & Medium & Limited by calibration \\ [PERSON] et al. [60] & Yes & Yes & No & Yes & PF & Floor & 90 th & 6 & Medium & Limited by calibration \\ [PERSON] et al. [61] & Yes & Yes & No & Yes & PF & Building & 90 th & 2.3 & Medium & Limited by calibration \\ [PERSON] et al. [62] & Yes & Yes & No & Yes & KF & 3800 & RMSE & 1 & Medium & Limited by calibration \\ [PERSON] et al. [63] & Yes & Yes & Yes & No & EKF & 8400 & RMSE & 2.9 & Medium & Limited by calibration \\ [PERSON] et al. [67] & Yes & Yes & No & No & EKF & 6000 & RMSE & 1.4-3.4 & Low & Yes \\ [PERSON] et al. [65] & Yes & Yes & No & No & PF & 600 & Mean & 0.6 & Medium & Limited by calibration \\ [PERSON] et al. [66] & Yes & Yes & No & Yes & KF & 425 & RMSE & 1.28 & Low & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Smartphone positioning systems.