On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization

Abstract

Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as simple signal strength prediction models, provide fast setup times, but often do not provide the accuracy required for use cases like indoor navigation or location-based services. While more complex models provide an increased accuracy by including architectural knowledge about walls and other obstacles, they often require additional computation during runtime and demand prior knowledge during setup. Within this work, we will thus focus on simple, easy to set up models and evaluate their performance compared to real-world measurements. The evaluation ranges from a fully-empiric, instant setup, given that the transmitter locations are well known, to a highly optimized scenario based on some reference measurements within the building. Furthermore, we will propose a new signal strength prediction model as a combination of several simple ones. This tradeoff increases accuracy with only minor additional computations. All of the optimized models are evaluated within an actual smartphone-based indoor localization system. This system uses the phone's Wi-Fi, barometer and IMU to infer the pedestrian's current location via recursive density estimation based on particle filtering. We will show that while a 100 % empiric parameter choice for the model already provides enough accuracy for many use cases, a small number of reference measurements is enough to dramatically increase such a system's performance.

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

“…The least-squares estimation produces a single point as position estimate by minimizing the quadratic error in (8). The optimization problem is based on the assumption in (7) that the measured distance to an AP is equal to the Euclidean distance between the AP and the current position.…”

“…For this reason, simplified models without intersection checks and precisely determined parameters are often used for the calculation. The positioning errors achieved in this case are often in a reasonable medium single-digit range of meters [8,9].…”

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

“…As a result, the accuracy and range of such positioning systems are heavily reliant on the numbers of base stations, which means that it may be financially costly to achieve a large-scale or high-precision implementation. Moreover, there are many challenges in obtaining accurate positioning through wireless signals due to the multipath effects and decay in obstacles [7][8][9]. It is also worth noting that if personal mobile phones are employed, users will have to face privacy problems involving personal information security [10].…”

On the Right Track: Comfort and Confusion in Indoor Environments

“…A considerable amount of research on indoor navigation has been published, but the subject is treated separately in different research fields, ranging from engineering (indoor positioning) (e.g., [6][7][8][9][10][11][12][13][14][15]), informatics (indoor modeling) (e.g., [16][17][18][19]), and architectural design (e.g., [20][21][22][23]), to psychology (analysis of spatial-related perceptual and cognitive processes) (e.g., [24][25][26][27][28]). Much of the current literature on navigation systems pays particular attention to the localization or other components of the system, while there has been little discussion about the optimization of route calculations.…”