Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones

Abstract

Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%.

Particle filtering supported probability density estimation of mobility patterns

“…For example, the weights of the position particles can be influenced by factors such as proximity to objects [43] or the sequence of road segments [44] . Furthermore, the weights can be set to zero or close to zero in cases where certain displacements are not feasible, such as crossing a wall [45] , [46] , [47] . Incorporating knowledge about both the dynamics of the movement process and the environmental factors that affect movement enables a more accurate localization.…”

Magnetic Field Positioning Technology of Indoor Sports Bodies

“…Compared with using a single sensor, the average heading error is as small as 4.72° [29]. Grottke, Jan's team combines inertial navigation unit and fingerprint based positioning unit, provides indoor positioning service based on smart phone with the help of sensors, wireless LAN, Bluetooth and other devices, and uses Bayesian filtering method to weight the position estimated by multiple sensors [30].…”

A Comparison of Distribution Models for Fast Variations in the Indoor Radio Channel at 5G Frequency Range 1 Microwave Bands

“…Triangulation-location based on WiFi signals, also in the 2.4 GHz band, are analyzed by [14], using again the Weibull model for predicting indoor fast variations in the signal level. There are other alternative strategies for such location applications [15], but they are out of the scope of this proposal.…”