Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks

Atia, Mohamed; Noureldin, Aboelmagd; Korenberg, Michael J.

doi:10.1109/tmc.2012.143

Cited by 130 publications

(86 citation statements)

References 29 publications

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“…This approach is based on a time-varying RSS variation model and a spatial correlation-based channel estimation [227], which can be done by GP [228]. While other works assume a constant RSS variation for all the APs, the solution in [72] presumes that the RSS variation for each AP varies independently in order to consider different propagation conditions over time and space.…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: B Fingerprint Matchingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

View full text Add to dashboard Cite

show abstract

“…The output of the formulation above, ω i , can be used to estimate the corresponding position by Equation (9).…”

Section: Ieee Icc 2014 -Mobile and Wireless Networking Symposiummentioning

confidence: 99%

“…Then the solution to this optimization problem ensures that the weight assigned to each reference point in estimation can jointly minimize the above difference in signal map and satisfy the sector constraints. In other words, the RPs that have small signal difference and lie in junction of sectors will obtain higher weights in Equation (9) and contribute more in target estimation. Hence in the minimization problem, the influence from the wrong nearest neighbors (corresponding RPs) in signal space is reduced.…”

Section: Ieee Icc 2014 -Mobile and Wireless Networking Symposiummentioning

confidence: 99%

Sectjunction: Wi-Fi indoor localization based on junction of signal sectors

Chan

2014

2014 IEEE International Conference on Communications (ICC)

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Abstract-In Wi-Fi fingerprint localization, a target sends its measured Received Signal Strength Indicator (RSSI) of access points (APs) to a server for its position estimation. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in database. Due to signal measurement uncertainty, this matching process often leads to a geographically dispersed set of reference points, resulting in unsatisfactory estimation accuracy.We propose a novel, efficient and highly accurate localization scheme termed Sectjunction which does not lead to a dispersed set of neighbors. For each selected AP, Sectjunction sectorizes its coverage area according to discrete signal levels, hence achieving robustness against measurement uncertainty. Based on the received AP RSSI, the target can then be mapped to the sector where it is likely to be. To further enhance its computational efficiency, Sectjunction partitions the site into multiple area clusters to narrow the search space. Through convex optimization, the target is localized based on the cluster and the junction of the sectors it is within. We have implemented Sectjunction, and our extensive experiments show that it significantly outperforms recent schemes with much lower estimation error.

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“…To reduce the maintenance effort of the training phase, in [9] the signature map is estimated dynamically based on the runtime measurements. For this purpose, neighboring APs exchange RSS measurements and utilize Gaussian regression to model the path loss and the corresponding RSS values.…”

Section: Related Workmentioning

confidence: 99%

“…For this purpose, neighboring APs exchange RSS measurements and utilize Gaussian regression to model the path loss and the corresponding RSS values. Although [9] offers a recalibration phase, the system requires specific hardware. In contrast, our approach does not require any extra hardware.…”

Section: Related Workmentioning

confidence: 99%

Efficient recalibration via Dynamic Matrix Completion

Nikitaki

Tsagkatakis

Tsakalides

2013

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

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Fingerprint-based localization techniques have witnessed significant progress as they provide highly accurate location estimation with minimal hardware interventions. However, the required calibration phase is time and labour consuming. In this work, we propose a reduced effort recalibration technique for fingerprint-based indoor positioning systems. Particularly, we reduce the number of received fingerprints by performing spatial sub-sampling. The recovery of the full map from partial measurements is formulated as an instance of a Dynamic Matrix Completion problem where we exploit the spatio-temporal correlations among the fingerprints. Analytical studies and simulations are provided to evaluate the performance of the proposed technique in terms of reconstruction and location error.Index Terms-matrix completion, fingerprint-based indoor localization, recalibration, received signal strength measurements, spatio-temporal correlations.

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Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks

Cited by 130 publications

References 29 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Sectjunction: Wi-Fi indoor localization based on junction of signal sectors

Efficient recalibration via Dynamic Matrix Completion

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