Indoor Positioning System Using Dynamic Model Estimation

Abstract

Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% better than a fixed-parameters model from the literature.

A Small World Graph Approach for an Efficient Indoor Positioning System

“…They are very efficient in this regard since no searches in datasets are required. However, they have lower accuracy [ 13 , 23 ]. Similarly, probability-based approaches are usually more accurate than geometrical ones, but they are still not as precise as fingerprint-based methods for larger and more complex environments [ 24 , 25 ].…”

“…For this, we proposed and implemented the SmartCampus Architecture, depicted in Figure 5 . In this architecture, beacon nodes (worn by the students) send BLE advertisement packets [ 13 , 55 ]. These packets are received by several Bluetooth-enabled devices, called scanners (similar to routers in WiFi IPSs).…”

Indoor Positioning System Using Synthetic Training and Data Fusion

“…In model-based IPSs, the signals measured between the mobile device and at least three anchor nodes are used to estimate the distances. For this, signal propagation models such as the Log-distance [10] and Two-ray Ground Reflection Model (TGRM) [17] are used. In [18], the authors use the frequency diversity in the wireless channel to reduce the multipath effect on the distance estimation.…”

“…The model-based IPS mentioned above uses a fixed path-loss exponent to characterize the signal behavior in all regions of the scenario, which is not a suitable solution for large-scale scenarios. In [10], the authors have performed a smaller training to find different path-loss exponents that characterize each region of the scenario. The results show that using dynamic model parameters decreases the positioning error.…”

“…Most IPSs can be classified into model-based and fingerprint-based. Model-based IPSs estimate the positions based on the distance between the mobile device and the Anchor Nodes (ANs), which are fixed devices with known positions [10]. For this, the RSSI values are converted into distances using a path-loss signal propagation model, the most known being the Log-Distance model.…”