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

Majeed, Khaqan; Sorour, Sameh; Al-Naffouri, Tareq Y.; Valaee, Shahrokh

doi:10.1109/tmc.2015.2510631

Cited by 35 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing our results to the ones reported in [15], [17] and [18], the proposed joint FbP and RM generation seems to provide better results in terms of both positioning and RSS estimation error (Table II). Regarding the positioning error, the mean error of the proposed approach is three times lower than that of distance based inter/extrapolation method in [15] and more than two times lower than that of manifold alignment based method in [18]. The mean positioning error is 20% lower than that of the compressive sensing based approach in [17].…”

Section: Results With Variational Inferencesupporting

confidence: 64%

“…There are few publications addressing simultaneous FbP and RM generation. Feng et al [17] and Majeed et al [18] employ compressive sensing with L 1 regularization and manifold alignment with geometry perturbation to achieve both FbP and RM generation. In [12], Zhou et al propose a method using NNs with backpropagation to realize joint position estimation and RM generation.…”

Section: B Joint Fbp and Rm Generationmentioning

confidence: 99%

See 1 more Smart Citation

Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS

Zhou

2017

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS and achieving the position estimation by reusing the available WLAN and mobile devices, and capable of providing absolute position estimation. For fingerprinting based positioning (FbP), a model is created to provide reference values of observable features (e.g., signal strength from access point (AP)) as a function of location during offline stage. One widely applied method to build a complete and an accurate reference database (i.e. radio map (RM)) for FWIPS is carrying out a site survey throughout the region of interest (RoI). Along the site survey, the readings of received signal strength (RSS) from all visible APs at each reference point (RP) are collected. This site survey, however, is time-consuming and labor-intensive, especially in the case that the RoI is large (e.g., an airport or a big mall). This bottleneck hinders the wide commercial applications of FWIPS (e.g., proximity promotions in a shopping center). To diminish the cost of site survey, we propose a probabilistic model, which combines fingerprinting based positioning (FbP) and RM generation based on stochastic variational Bayesian inference (SVBI). This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation. These benefits make it outperforms the previous proposed approaches. We validate the proposed approach via experimental simulation and analysis. Compared to the FbP approaches based on a single layer neural network (SNN), deep neural network (DNN) and k nearest neighbors (kNN), the proposed SVBI based position estimation outperforms them. The reduction of root mean squared error of the localization is up to 40% comparing to that of SNN based FbP. Moreover, the cumulative positioning accuracy, defined as the cumulative distribution function of the positioning errors, of the proposed FbP and kNN are 92% and 84% within 4 m, respectively. The improvement of the positioning accuracy is up to 8%. Regarding the performance of SVBI based RM generation, it is comparable to that of the manually collected RM and adequate for the applications, which require the room level positioning accuracy.

show abstract

Section: Results With Variational Inferencesupporting

confidence: 64%

Section: B Joint Fbp and Rm Generationmentioning

confidence: 99%

Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS

Zhou

2017

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

show abstract

“…Calibration of APs gains can be done in the fabrication process or at setup time which takes into account the antenna coupling with the environment, or calibration can be run jointly to positioning as an unsupervised learning process. Many works deal with the calibration of radio map with pure machine learning [24][25][26][27], where others introduce path-loss model into the learning process [17,24,28]. It is difficult to find how much radio map learning can improve the positioning performances and what will be quantitatively the positioning error after the learning phase.…”

Section: Introductionmentioning

confidence: 99%

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

et al. 2019

View full text Add to dashboard Cite

In this paper we propose a global positioning algorithm of multiple assets based on Received Signal Strength (RSS) measurements that takes into account the gain uncertainties of each hardware transceiver involved in the system, as well as the uncertainties on the Log-Distance Path Loss (LDPL) parameters. Such a statistical model is established and its Maximum Likelihood Estimator (MLE) is given with the analytic expression of the Cramér-Rao Lower Bound (CRLB). Typical values of those uncertainties are given considering whether calibration is done in production, in situ, or if hardware is used uncalibrated, in order to know what is the expected accuracy in function of the calibration setup. Results are tested by numerical simulations and confronted to real measurements in different room configurations, showing that the theoretical bound can be reached by the proposed MLE algorithm.

show abstract

“…One solution is proposed in [ 13 ], which considers having an incomplete fingerprint radio map with realistic coverage gaps, and studies the performance of several interpolation methods for recovering the missing fingerprint data. In the localization framework using unsupervised manifold alignment [ 14 ], it requires very little of the fingerprinting load, some crowdsourcing-based RPs, and plan coordinates of the indoor area. Further, without building a full fingerprinted radio map of the indoor environment, a scheme tries to construct the radio map with limited calibration [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

EKF–GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity

Yang

Cheng

et al. 2018

Sensors

View full text Add to dashboard Cite

Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper, we describe a Fingerprint Renovation System (FRS) based on crowdsourcing, which avoids the use of manual labour to obtain the up-to-date fingerprint status. Extended Kalman Filter (EKF) and Gaussian Process Regression (GPR) in FRS are combined to calculate the current state based on the original fingerprinting radio map. In this system, a method of subset acquisition also makes an immediate impression to reduce the huge computation caused by too many reference points (RPs). Meanwhile, adjusted cosine similarity (ACS) is employed in the online phase to solve the issue of outliers produced by cosine similarity. Both experiments and analytical simulation in a real Wireless Fidelity (Wi-Fi) environment indicate the usefulness of our system to significant performance improvements. The results show that FRS improves the accuracy by 19.6% in the surveyed area compared to the radio map un-renovated. Moreover, the proposed subset algorithm can bring less computation.

show abstract

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

Cited by 35 publications

References 26 publications

Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS

Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

EKF–GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity

Contact Info

Product

Resources

About