# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 3 Methodology
### Proposed Framework  
The proposed framework that was implemented in this study is an extension of typical RSSI fingerprinting localization, augmented with sensor data for phone device pose classification, as shown in Figure 3.  
The accelerometer sensor was chosen to detect the smartphone device pose because it is the most common sensor used for the identification of human activity classes [11; 12] and in sole device pose identification [18]. It is extremely accurate (>90%) for both applications; in refs. [19; 20], the authors compared the use of different sensors individually and together for device-pose-only identification, and both studies concluded that using multiple sensors (together with, or aside from, the accelerometer) does not significantly increase accuracy compared with using the accelerometer alone.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Framework** & **Advantages** & **Disadvantages** \\ \hline Pedestrian dead reckoning (PDR) fusion with RSSI using standard data fusion algorithms (e.g., Kalman filter) [6; 9; 10] & \(\bullet\) & Good accuracy improvement \\ \hline Accelerometer-based activity recognition for: & & & \(\bullet\) & Accumulation of dead-reckoning error [11] \\  • Anchor selection strategy [13] based on movement & & & \(\bullet\) & Direction of travel resolution impossible without constraints [11; 12], making practical use difficult. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Framework** & **Advantages** & **Disadvantages** \\ \hline Pedestrian dead reckoning (PDR) fusion with RSSI using standard data fusion algorithms (e.g., Kalman filter) [6; 9; 10] & \(\bullet\) & Good accuracy improvement \\ \hline Accelerometer-based activity recognition for: & & & \(\bullet\) & Accumulation of dead-reckoning error [11] \\ • Anchor selection strategy [13] based on movement & & & \(\bullet\) & Direction of travel resolution impossible without constraints [11; 12], making practical use difficult. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Framework** & **Advantages** & **Disadvantages** \\ \hline Pedestrian dead reckoning (PDR) fusion with RSSI using standard data fusion algorithms (e.g., Kalman filter) [6; 9; 10] & \(\bullet\) & Good accuracy improvement \\ \hline Accelerometer-based activity recognition for: & & & \(\bullet\) & Low accuracy improvement, even reduction in accuracy (e.g., [16]) \\ • Anchor selection strategy [13] based on movement & & & \(\bullet\) & Requires additional data collection (e.g., static/ dynamic [17]) for training. \\ • Anchor selection strategy [13] based on movement & & & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of some frameworks in the literature for combining accelerometers and RSSI for localization.  
During the training phase, RSSI measurements were taken at various locations in the region (such as zones on the floor of a building) and stored in a database. Additionally, during this training phase, phone accelerometer measurements were recorded for each use case under consideration. These data were then used to train classifiers in a supervised learning approach for the phone's device pose and the user's location. During the online/testing phase, the new RSSI measurements and phone accelerometer measurements were processed through an integrated trained classifier, and a location output class was determined.