Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process

Abstract

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

“…The RSSI is a function of the distance between the transmitter and the receiving device, which varies due to kinds of in-path interference [ 32 ]. According to [ 33 , 34 ], the log-distance path loss (LDPL) model is typically adopted to map RSSI to propagation distance d : where is the transmission power in dBm, PL ( ) is the path loss at reference distance , and is the path loss exponent. is a zero-mean normal random variable reflecting shadowing attenuation in dB.…”

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

“…Several fingerprinting systems based on sample crowdsourcing have been proposed for indoor localization in previous studies [ 13 , 14 , 15 , 16 , 17 , 18 ]. For example, Chen and Wang [ 13 ] proposed using a density-based clustering technique to group crowdsourced samples to generate a cluster fingerprint and using a matching algorithm to assign each cluster fingerprint to one subarea for room-level localization.…”

“…Liu et al [ 14 ] also applied crowdsourced samples for room-level localization yet with an improved energy-efficient sampling approach. Chang et al [ 15 ] applied a local Gaussian process to construct grid fingerprints from crowdsourced samples. Jung et al [ 16 ] adopted a hybrid global-local optimization scheme to determine the location of fingerprint sequences based on the constraint of the indoor structure, rather than using labeled fingerprints.…”

Gaussian Process Regression Plus Method for Localization Reliability Improvement

“…GPs are the extension of multivariate Gaussian model to the infinite-sized vector of real-valued variables. The GP is fully specified by a mean function and a covariance function such as: $\{\begin{array}{c}m\left(x\right)=E\left[f\left(x\right)\right]\\ k\left(x|x^{\prime }\right)=E[\left(f\left(x\right)-m\left(x\right)\right)\left(f\left(x^{\prime }\right)-m\left(x^{\prime }\right)\right)]\end{array}$ where $x\text{ and }{x}^{\prime }ϵR$ are random variables [19]. The GP equation is $\mathrm{f}\left(x\right)~gp\left(m\left(x\right),k\left(x,x^{\prime }\right)\right)$.…”