# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 4 Hybrid Positioning Systems
### Map Hybrid Systems  
The high complexity of the indoor environments with different distributions of walls and furniture that produces NLOS communications between the user and the wireless networks is an inconvenient for IPS because it produces less accurate estimations of the position. However, if the map of the building is a priori known by the user, the high complexity can be an advantage to the IPS as it can constrain the possible positions and improve the accuracy of the estimations. Commonly, the map information is used to enhance the performance of the RSS-IMU hybrid systems. Figure 11 shows the estimated trajectory from an inertial system that is affected by drift and how the map information can help us to recover the original path. Typically, the map information is included in the fusion of the measurements using a particle filter. During the calculation of the weights of each particle, the map constraints are calculated and those particles that have been propagated to impossible locations (as for example crossing a wall) receive a weight of zero preventing the resampling of this particle in the following epoch. For example, in [51] the measurements of an RSS probabilistic fingerprinting are combined with the measurements of a belt mounted SHS. Then a PF fuses the measurements with the map information. Similarly, in [52] the authors use an equivalent system but the IMU is placed on the foot of the user. There are other examples of hybrid systems with map information as in [53; 54]. Table 4 summarizes all of them for the purpose of comparison including the main characteristics of the underlaying systems employed and the experimental evaluation. Figure 11: Inertial position estimation with drift (red) and corrected path (dashed). \begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Technologies**} & \multirow{2}{*}{**RSS**} & \multicolumn{2}{c}{**IMU**} & \multirow{2}{*}{**Anchors**} & \multirow{2}{*}{**Area (m\({}^{2}\))**} & \multicolumn{2}{c}{**Error**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Scalability**} \\ \cline{5-5} \cline{7-10}  & & & **Position** & & & & & & **Type** & **Value (m)** & \\ \hline [PERSON] et al. [51] & WiFi, MEMS & Fingerprinting & Belt & SHS & 4 & 1600 & RMSE & 1.53 & Medium & Limited by calibration \\ [PERSON] et al. [52] & WiFi, MEMS & Fingerprinting & Foot & SHS & 33 & 8725 & 90 th & 0.73 & Medium & Limited by calibration \\ [PERSON] et al. [53] & WiFi, MEMS & Fingerprinting & N/A & Step & 5 & 1000 & RMSE & 4.3 & Medium & Limited by calibration \\ [PERSON] et al. [54] & WSN, MEMS & Proximity & Belt & SHS & 9 & Floor & RMSE & 1.2 & Low & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Map hybrid positioning systems. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Drift**} & \multicolumn{3}{c}{**Cost**} & \multirow{2}{*}{**Calibration**} & \multirow{2}{*}{**Integration with Network**} & \multirow{2}{*}{**Hardware**} & \multirow{2}{*}{**Scalability problems**} \\ \cline{5-5} \cline{7-10}  & & & **Computational** & & & & & & & \\ \hline Time & High & No & Low & Medium & No & Yes & Transceiver, accurate clocks & Synchronisation \\ Angle & High & No & Low & Medium & No & Yes & Transceiver, multiple antennas & Synchronisation \\ RSS & Low & No & Low & Low & Easy & No & Transceiver & No \\ Proximity & Poor & No & Low & Low & No & No & Transceiver & No \\ Fingerprinting & Medium & No & High & Low & Laborious & No & Transceiver & Calibration \\ PDR & High & Yes & Medium & Low & No & No & IMU & No \\ SLAM & High & Closed loops & High & Low & No & No & IMU & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Indoor Positioning Systems.