Indoor Localization Using Visible Light Via Fusion of Multiple Classifiers

Abstract

IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Abstract: We propose a localization technique by fusing multiple classifiers based on received signal strengths (RSSs) of visible light in which different intensity-modulated sinusoidal signals emitted by LEDs are captured by photodiodes placed at various grid points. First, we obtain some approximate RSSs fingerprints by capturing the peaks of power spectral density of the received signals at each given grid point. Unlike the existing RSSs-based algorithms, several representative machine learning algorithms are adopted to train multiple classifiers based on these RSSs fingerprints. Then, two robust fusion localization algorithms, namely, grid-independent least square and grid-dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. A singular value decomposition (SVD)-based LS (LS-SVD) method is proposed to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on an intensity-modulated direct detection system show that the probability of having mean square positioning error of less than 5 cm achieved by GD-LS is improved by 93.03% and 93.15%, respectively, as compared to those by the RSS ratio and RSS matching methods with the fast Fourier transform length of 2000.Index Terms: Indoor positioning, visible light communications (VLC), received signal strengths (RSSs) fingerprints, intensity modulated direct detection (IM/DD), machine learning, fusion localization.

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning

“…The RSS at frequency f i is used to estimate the distance between the detector and the ith LED. The received signal s n (t) of the nth detector at (x n , y n , z L h − ) from all the LEDs can be expressed as [10,21]:…”

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

“…To overcome these challenges, it is necessary to develop alternative localization systems for indoor localization. Existing indoor localization systems are based on ultra-wideband (UWB) technology [3], pedestrian dead reckoning (PDR) [4], radio frequency identification (RFID) [5], Bluetooth [6], visible light communication (VLC) [7], Zigbee [8] and Wi-Fi systems [9]. Each of these techniques achieves high position accuracy for indoor localization.…”

Implementation of a VLC‐based indoor localization system

“…Some of these proposals are used together with frequency ID to distinguish the emitting sources. This frequency ID process consists of transmitting a modulated signal, which has a different carrier signal for each node . An advantage of this process is that the receiver can obtain reference points for the minimum and maximum power of the signal, which makes it useful for canceling the environment's light.…”

“…After this stage, an existing localization algorithm is performed using the collected fingerprint information to improve the results. One example of working with fingerprints is presented in the work of Guo et al; on the setup process, the RSSI is measured on different points of the room. Each measure is represented as a signal or function of time with a significant number of samples.…”

“…This frequency ID process consists of transmitting a modulated signal, which has a different carrier signal for each node. [6][7][8][9] An advantage of this process is that the receiver can obtain reference points for the minimum and maximum power of the signal, which makes it useful for canceling the environment's light. This method has a good precision range without additional hardware.…”