Distance Estimation Research Papers - Academia.edu (original) (raw)

Nowadays most of commonly used positioning and location detection systems, accessible via mobile phones, are usually targeted to open spaces, since adopted technologies are mainly based on GPS and satellite tracking, which are robust for outdoor environments. Only in recent years the focus has passed to indoor venues where people spend about 80% - 90% of their time. The goal of indoor positioning in some applications, notably oriented to hospitals and malls, is to monitor people into a structure or provide navigation support, others want to use indoor positioning to better understand how customers behaves, enhance their satisfaction, branding and marketing for the venue, provide just-in-time information (for instance, intelligent audio guides for tours) or offer, by the means of location information, video or augmented reality experiences or connect people of interest in proximity to each another. In other words, indoor positioning systems (IPSs) enable location-awareness for mobile devices in ubiquitous and pervasive wireless computing systems. The need for connectivity, access and navigation has fueled research and investments in this field. A different and innovative solution for developing an indoor positioning system (IPS) could be based on the adoption of a low-cost transmitting infrastructure, based on a limited number of Bluetooth Low Energy (BLE) beacons, not requesting to the installer any effort (no on-site surveys) by defining a real-time and environment-adaptive signal propagation model, based on the evolution of Received Signal Strengths Indicator over time. Accordingly, the present work aims at advancing knowledge about a feasible adoption of BLE for positioning purposes in restricted (room, offices, etc.) ranges. To do so, the evolution and stability of Bluetooth Low Energy signal is studied is firstly evaluated in order to validate its relationship with respect to changing distances between transmiter and receiver. Secondly, a dynamic model based on Bayesian filtering techniques to detect model parameters is proposed. The biggest difference between Bayesian method and the traditional positioning methods lies in the fact that Bayesian method does not focus on obtaining a more accurate and stable RSSI signal from each beacon (which seems complex for this kind of signal), but on obtaining a more reliable sample collection, and through continuous updating of particles and related weights, eventually, it will converge to the most probable propagation model parameters distribution. Results show an increased accuracy with respect to other commonly widespread methods, based on linear regression on the curve describing the relation between listened power and distance.