Joint Indoor Localization and Radio Map Construction with Limited Deployment Load

Abstract

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment under study. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environments plan coordinates. For moving users, we exploit the correlation of their observations to improve the localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieves better results than the simulated radio maps, and a negligible degradation with 70-85% reduction in calibration load.

Indoor Localization and Radio Map Estimation Using Unsupervised Manifold Alignment with Geometry Perturbation

“…An interpolation function is used to give the (x, y) coordinates as a function of RSS values. Another recent example employs semisupervised manifold alignment (SMA) [23] to solve the localization problem in the presence of a limited number of fingerprints [28], [29]. In semi-supervised localization approaches, a small percentage of the RSS fingerprints is obtained throughout the indoor area and are termed as labeled data (calibration information/data/readings).…”

“…Some solutions [5]- [8] estimate location by collecting readings from inertial sensors (gyroscope, compass, and accelerometer) present in the mobile devices. Semi-supervised manifold alignment (SMA) [23] is used in [28], [29] to localize users. In this work, we aim to further reduce the fingerprinting load (1-5%) while still maintaining low degradation in performance.…”

Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

“…From these figures, we can find that more than 80% mean errors are smaller than 3 m, and meanwhile, the localization accuracy can be well guaranteed under most of the replications for the physical coordinates augmentation. Figure 4f compares the CDFs of errors of the target by using the proposed and other four popular approaches, i.e., WKNN, Bayesian, kernel and the conventional manifold alignment without transformation matrices [28]. From this figure, we can find that the proposed approach outperforms the other four in localization accuracy, which demonstrates the significant benefit provided by the dimension expansion to the manifold alignment for indoor localization.…”

“…Additionally, the locations of the target are estimated by matching the newly-collected RSS measurements against the radio map in a low-dimensional manifold. By employing the unsupervised manifold alignment approach, the authors in [28] utilize the WLAN RSS fingerprints and newly-collected RSS measurements to construct a source dataset and meanwhile rely on the coordinates of RSS fingerprints to construct a destination dataset. After performing the transformation from the source into destination datasets, the target is located based on the neighbor matching of the transformed data with respect to the newly-collected RSS measurements and coordinates of RSS fingerprints in the common manifold.…”

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

“…Indoor location-based services (LBS) such as indoor positioning, tracking and navigation, have been receiving a lot of attention in recent years [1,2]. However, it remains a challenge to provide the users with an accurate and robust location estimation.…”