Magnetic Field Feature Extraction and Selection for Indoor Location Estimation

Abstract

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

“…Their location, construction materials, electrical circuits and some of the walls are shared between rooms, making common spaces, which adds similar readings between two rooms, adding complexity to the development. To ensure statistical validity, the minimum number of magnetic field signatures for each room was determined using Equation (4), as proposed by Galvan-Tejada et al [18]. In this equation the result represented by x is the number of signatures to develop the model, N is the number of variables used for the experimentation.…”

“…In the case of environmental sound in a non-controlled environment, any type of noise could led to error estimation, additionally to the noise or offset that may be added by the microphone. Therefore, with the development and the availability of magnetic sensors in common devices, such as smartphones, approaches that use magnetic field signal as information source has been proposed [18][19][20] given that it is a robust signal to natural earth phenomena as rotation and translation [18].…”

Accurate indoor positioning with ultra-wide band sensors

“…When the GPS signals cannot be reached, it is possible to use a different technology to determine the coordinates of indoor location by means of infrared, ultrasonic, cellular, radio frequency identification (RFID), wireless networks (Wi-Fi), Bluetooth Low Energy (BLE) beacons or Ultra-wide band (UWB) [4][5][6]. In some studies, even visible light has been used [7][8][9] as well as technologies that utilize the Earth's magnetic field [10,11]. Indoor positioning systems have been developed rapidly in recent decades.…”

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

“…Magnetic anomaly signals can be used to invert the target parameters—i.e., position and magnetic moment—which have many applications such as unexploded ordnance detection [3,4,5], underwater magnetic tracking [6,7], intruder detection [8], biomedical applications [9], and indoor localization [10]. Usually magnetic anomaly signals are much weaker than the geomagnetic field intensity and they cannot be measured by magnetometers directly.…”