Robust floor determination for indoor positioning

Gupta, Pankaj; Bharadwaj, Sachin; Ramakrishnan, S.; Balakrishnan, J.

doi:10.1109/ncc.2014.6811285

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Authors in [158] also consider the use of pressure data to improve a Wi-Fi RSS-based floor detection solution. In their work, pressure data is used to calculate the value of a Vertical Motion Indicator which, in turn, is used to detect when a user is moving to a different floor, blending that information with the output of the RSS-based floor estimator.…”

Section: Sensor-based Solutionsmentioning

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: Sensor-based Solutionsmentioning

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

“…Next, in the localization process, we investigate the existing indoor positioning research that has focused on robust localization estimation algorithms. e authors of [18,19] presented robust floor determination algorithms that considered the movement of objects across the floors. Gupta et al [18] studied robots moving between floors by using incorporative information from the pressure sensor and the Wi-Fi access points (APs).…”

Section: Related Workmentioning

confidence: 99%

“…e authors of [18,19] presented robust floor determination algorithms that considered the movement of objects across the floors. Gupta et al [18] studied robots moving between floors by using incorporative information from the pressure sensor and the Wi-Fi access points (APs). eir floor determination algorithm is based on the RSS received from the APs that utilize a Maximum Likelihood (ML) to estimate the floor location of the robots.…”

Section: Related Workmentioning

confidence: 99%

“…According to the literature reviewed on the robust localization process, although some existing works have studied the robust and reliable location problem [18][19][20][21][22][23], they focused on robustness issues, including robust tracking of floor changes when the robots moved in the staircase, improving the robustness of dynamic changes to the indoor environment which lead to the instability of fingerprint information, and reducing the influence of different mobile Single floor and multifloor Yes devices. In particular, they did not consider the robustness in terms of faulty RNs during the online estimation phase.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Maneerat

Kaemarungsi

2019

Mobile Information Systems

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Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

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“…Floor detection probabilities above 90% are reported in [17] and [18]. However, this achievable performance is strongly affected by the AP availability, the quality of the RSS measurements and the localization technique, which may lead to the use of sensors to avoid outages [19]. In addition, fingerprinting methods require a calibration or wardriving phase, where the RSS measurements are collected, assigned to a specific location, and recorded in a database.…”

Section: Wi-fi (Ieee 80211)mentioning

confidence: 99%

Floor Detection with Indoor Vertical Positioning in LTE Femtocell Networks

Peral-Rosado

Bavaro

López-Salcedo

et al. 2015

2015 IEEE Globecom Workshops (GC Wkshps)

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Vertical positioning is nowadays a topic of high interest in indoor environments, due to the stringent requirements on indoor location accuracy for emergency calls, such as E911. Thus, indoor positioning techniques are investigated to achieve floor detection in order to fulfill these legal mandates. The use of Long Term Evolution (LTE) heterogeneous networks is an attractive solution due to the combination of communications and positioning capabilities. This paper provides an overview of the existing floor detection techniques. Practical results using Global Navigation Satellite Systems (GNSS), inertial sensors, barometer, and LTE signals are obtained in an experimental LTE femtocell network, within a deployment in a two-story building. Probabilities of floor detection above 67% of the cases are found for the positioning solutions assessed.

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Robust floor determination for indoor positioning

Cited by 14 publications

References 9 publications

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

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

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Floor Detection with Indoor Vertical Positioning in LTE Femtocell Networks

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