# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 2 Related Work and Motivation  
Using inertial measurement unit (IMU) sensors on a smartphone or as a separate wearable for user-behavior detection has been copiously researched as a complementary data modality to improve indoor localization system accuracy [5], and most studies [8] have examined pedestrian dead reckoning (PDR) as the data fusion approach utilizing methods such as filtering [6; 9; 10]. However, smartphone-based PDR is also known to have major drawbacks, such as the accumulation of errors over time [11] and the impossibility of resolving the direction of travel without constraining the location or orientation of the smartphone (which is still an open research question [11; 12]). The latter is an especially important consideration when looking at indoor localization systems because they are used in a diverse range of settings such as hospitals and shopping malls, in which users cannot be constrained to holding their phone in a particular manner. So, while smartphone-based PDR techniques to improve indoor localization accuracy may provide good results in studies, they may never be suitable for practical implementation. The phone's device pose (i.e., how the phone is being carried/held by the user) changes the electromagnetic channel and affects the RSSI values at the anchors (even if the individual is not moving, as demonstrated in [13]) due to two easily identifiable reasons. Firstly, the phone may be placed in a bag or pocket or held at the ear, both of which cause varying magnitudes of greater attenuation due to shadowing compared with when the phone is held in an outstretched hand [14]. Secondly, being placed in a low carried handbag or being held at the ear can change the distance vector to an anchor and degrade performance. It should also be noted that these effects are compounded with distance. As described in a short experiment [15], a 10 dB attenuated RSSI at 10 cm away from an anchor produced about the same range estimate as unattenuated RSSI. However, at 1 m away from the anchor, a 10 dB attenuated RSSI could cause a difference in range estimate of 5-10 m compared with that of unattenuated RSSI. Thus, if during the fingerprinting measurement campaign, the range of RSSI values for different device poses at the zones are also collected, this could improve the stored fingerprint by accounting for the variations in the RSSI due to device pose in the zones. There are only a handful of recent studies that have explored such data-level conceptual frameworks to use the smartphone sensors in order to improve the underlying RF fingerprinting pattern recognition problem. In [16], the researchers used the concept of different unique activities being carried out in different rooms of typical living quarters (e.g., lying on a bed in a bedroom, cooking in a kitchen, etc.) to improve the accuracy of a BLE RSSI fingerprinting localization system. Smartphone sensor data were collected from individuals using the different rooms for their activities, and this room-level localization was combined with RSSI fingerprinting localization using various classifiers. This resulted in slight accuracy improvements for some of the rooms and a decrease in accuracy in others. In ref. [13], a BLE beacon selection strategy was developed to remove nearby beacons because it was found that the RSSI from these is most profoundly affected by small movements of the user's hand; however, the authors did not explore utilizing the device pose to improve the localization accuracy. Notably, in a recent study [17], the researchers used machine learning algorithms on accelerometer measurements to detect whether a user is moving (dynamic) or standing relatively still (static); thus, they proposed reliability metrics for the localization on RSSI values received while the user is static and dynamic. As such, the authors assessed the relationship between the user behavior component in Figure 1 and the RSSI localization in the experimental setup of five zones in a large room.