Wireless RSSI fingerprinting localization

Yiu, S.; Dashti, Marzieh; Claußen, Holger; Pérez-Cruz, Fernando

doi:10.1016/j.sigpro.2016.07.005

Cited by 173 publications

(104 citation statements)

References 38 publications

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“…Moreover, it is known that the denser the set of RPs and the larger the number of fingerprints collected at each RP, the better the accuracy of the position estimates, which calls for even harder work in building the radiomaps [70], [71]. Authors in [70] performed an experimental study where they measured the accuracy of a positioning system for several densities of the radiomap, namely by considering a different number of fingerprints collected at each RP.…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

“…They concluded that accuracy increases significantly as more fingerprints are collected. An evaluation on the impact of the radiomap density in the accuracy of the positioning system, using four different methods, was also performed in in [71]. The key finding was that a reduction on the density of RPs always results in an accuracy degradation, while also showing that some methods are more robust than others to a decrease of the radiomap density.…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

“…Moreover, RSS readings can be affected by RF interference from other electronic devices, e.g., microwave ovens or cordless phones operating on the same frequency, which calls for robust fingerprint matching algorithms, as discussed in [67]. Given the effort associated with creating and maintaining radiomaps, solutions based on crowdsourcing (discussed later in this section), SLAM (discussed in Section X), as well as parametric and non-parametric models have been proposed [71], [106]. In particular, radiomaps based on models can be obtained from a much sparser set of RSS fingerprints and have the potential of reducing the computation effort in estimating the position.…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

“…In particular, radiomaps based on models can be obtained from a much sparser set of RSS fingerprints and have the potential of reducing the computation effort in estimating the position. While model-based radiomaps usually result in accuracy degradation, models based on non-parametric Gaussian Process (GP) regression have been reported to provide better accuracy results than the traditional radiomap construction based solely on data collection [71].…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: B Fingerprint Matchingmentioning

confidence: 99%

Section: B Fingerprint Matchingmentioning

confidence: 99%

Section: B Fingerprint Matchingmentioning

confidence: 99%

See 2 more Smart Citations

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

“…GPS offers an excellent ability when used outdoors, but because of limitations in the use of the transmission line-of-sight (LOS), then the use of GPS is less effective when used indoors, especially in the multi-stored building [3]. One technology that can be utilized to recognize indoor locations is by using wireless technology (for example, using WIFI) contained within a building [4,5]. The method of determining location using wireless network can be done more quickly and be more efficient use of power source.…”

Section: Introductionmentioning

confidence: 99%

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

Dari

Suyoto

Pranowo

2018

Int. J. Interact. Mob. Technol.

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Abstract-The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS's fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

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Outdoor positioning based on deep learning and wireless network fingerprint technology

Lei

Zhang

et al. 2020

Int J RF Microw Comput Aided Eng

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Localization has attracted considerable attention in recent years because of the growth in location-based services and computer-aided technology. In particular, wireless fingerprint-based positioning techniques have received considerable attention due to their satisfactory positioning performance with smartphones. In this work, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. First, long-timeevolution (LTE) mobile networks are used to provide positioning information, and the collected measurements of LTE signals are converted into grayscale fingerprint images to construct the fingerprint database. To overcome the instability of LTE signals, several data enhancement methods are leveraged to increase the diversity of the fingerprint database. Second, a modified deep residual network (MResNet) coarse localizer is used to learn reliable features from the fingerprint image database. Then, inspired by the ideas of feature fusion and transfer learning, a multilayer perceptron (MLP)-based fine localizer is used to further learn the features of the fingerprint images and achieve a better positioning performance. Our experimental results convincingly reveal that the proposed positioning system can achieve satisfactory positioning performance in a variety of outdoor environments.

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Wireless RSSI fingerprinting localization

Cited by 173 publications

References 38 publications

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

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

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

Outdoor positioning based on deep learning and wireless network fingerprint technology

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