Wi-Fi/MARG/GPS integrated system for seamless mobile positioning

Liu, Xingchuan; Man, Qingshan; Lu, Henghui; Lin, Xiao

doi:10.1109/wcnc.2013.6554923

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…The main purpose of combining different technologies is to increase the accuracy [12], or the density of the devices to be localized [13], or to overcome the continuity indoor/outdoor challenge [14], where these systems need to handle heterogeneous devices [15] and vertical handovers [1]. European Project WHERE2 [16] presented results of real-life experiments based on ZigBee and orthogonal frequency division multiplexing (OFDM) devices (emulating a multistandard terminal moving in typical indoor environments), with measurements using Received Signal Strength Indicator (RSSI) and round-trip delay (RTD).…”

Section: State Of the Artmentioning

confidence: 99%

Enhancing RFID indoor localization with cellular technologies

Aguilar-Garcı́a

Fortes

Colin

et al. 2015

J Wireless Com Network

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Indoor localization, this means solutions providing the position of mobile objects/persons in indoor environments (e.g., hospitals, malls, etc.), is one of the most cutting-edge services with growing demand in smart applications such as robotics for care, pedestrian navigations, etc. With the objective of providing indoor localization, this paper presents the experimental analysis of simultaneous radio frequency measurements from two different radio frequency systems: Ultra High Frequency Radio Frequency IDentification (UHF RFID) and macrocellular networks. Extensively deployed cellular technologies (Global System for Mobile communications (GSM) and Universal Mobile Telecommunications System (UMTS)) are here evaluated with the purpose of enhancing pre-existent RFID-based localization systems at reduced costs. Temporal and statistical analysis of the measurements gathered from each technology is performed, and its applicability for localization is assessed. Based on this analysis, a RFID localization mechanism that is able to integrate macrocellular technologies information is proposed, showing improved results in terms of accuracy.

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Section: State Of the Artmentioning

confidence: 99%

Enhancing RFID indoor localization with cellular technologies

Aguilar-Garcı́a

Fortes

Colin

et al. 2015

J Wireless Com Network

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A Hybrid Indoor Localization System Running Ensemble Machine Learning

Phuong

Pham

2018

2018 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Ubiquitous Computing &Amp; Communications, Big

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Mobile robust localization based on KF using inertial sensor and chirp-spread-spectrum ranging

Liu

Sun

2015

2015 9th International Conference on Sensing Technology (ICST)

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Realizing a reliable and robust localization based on mobile nodes plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor robust positioning is still a challenging problem because of the unavailability of GPS and complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve the problem, an accurate and robust integration localization scheme based on Kalman filter is proposed in this paper. In the scheme, we merge the two heterogeneous but complementary positioning technologies on the mobile node equipped with both inertial sensors and the chirp-spread-spectrum ranging hardware. In order to NLOS identification and decrease NLOS error, a novel sight-state estimation method based on the Markov model is proposed. Besides, experiments have been carried out in real indoor NLOS environment to evaluate performance of proposed system. Experimental results indicate a remarkable performance improvement by using the proposed integrated system.

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Wi-Fi/MARG/GPS integrated system for seamless mobile positioning

Cited by 5 publications

References 16 publications

Enhancing RFID indoor localization with cellular technologies

Enhancing RFID indoor localization with cellular technologies

A Hybrid Indoor Localization System Running Ensemble Machine Learning

Mobile robust localization based on KF using inertial sensor and chirp-spread-spectrum ranging

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