A multi-pronged approach for indoor positioning with WiFi, magnetic and cellular signals

Kim, Byunghun; Kwak, Myungchul; Lee, Jeongkeun; Kwon, Ted Taekyoung

doi:10.1109/ipin.2014.7275551

Cited by 26 publications

(12 citation statements)

References 4 publications

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“…The RSRP computation is based on RSSI. It calculates mean obtainable strength by cell-specific reference signals [92]. Thus, it can afford greater signal strength information associated with various positions contrasting normal RSSI.…”

Section: Rsrp and Rsrqmentioning

confidence: 99%

“…However, fingerprinting localization algorithms give the best performance and attract the researcher's attention due to easy implementation, low complexity, no need for the LoS measurements of APs and specialized hardware [104]. The average localization errors are described as 2~3 m in Wi-Fi-based positioning algorithms [92]. Wireless signals of Wi-Fi access points can protect huge areas, however, they need multipart hardware and software collaboration with each other [38].…”

Section: Wi-fi Technologymentioning

confidence: 99%

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Review of Indoor Positioning: Radio Wave Technology

et al. 2020

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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Section: Rsrp and Rsrqmentioning

confidence: 99%

Section: Wi-fi Technologymentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

et al. 2020

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“…An AR maker is a squarebased fiducial marker, which can easily be recognized by simple image processing techniques. The coding of an AR marker guarantees that any AR symbol will maintain a nonzero Hamming distance with itself after rotation [47]. The precision of the estimation depends on the size of the AR marker and the resolution of the camera.…”

Section: Navigation Using Cognitive Mapsmentioning

confidence: 99%

Sight-to-Sound Human-Machine Interface for Guiding and Navigating Visually Impaired People

Yang

Saniie

2020

IEEE Access

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Visually impaired people often find it hard to navigate efficiently in complex environments. Moreover, helping them to navigate intuitively is not a trivial task. In sighted people, cognitive maps derived from visual cues play a pivotal role in navigation. In this paper, we present a sight-to-sound human-machine interface (STS-HMI), a novel machine vision guidance system that enables visually impaired people to navigate with instantaneous and intuitive responses. The proposed STS-HMI system extracts visual context from scenes and converts them into binaural acoustic cues for users to establish cognitive maps. A series of experiments were conducted to evaluate the performance of the STS-HMI system in a complex environment with difficult navigation paths. The experimental results confirm that the STS-HMI system improves visually impaired people's mobility with minimal effort. INDEX TERMS Acoustic cues, human-machine interface, navigation, visually impaired.

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“…Since all of these methods have their own advantages and disadvantages, multi-sensor fusion approaches have been researched by many people to take advantage of different methods. Kim et al [ 24 ] combined magnetic signals, Wi-Fi signals and cellular signals to realize indoor location. Jeon et al [ 25 ] proposed a method integrating Bluetooth RSSI with an accelerometer and a barometer on smartphones to reach higher positioning accuracy compared with the approach without Bluetooth RSSI.…”

Section: Related Workmentioning

confidence: 99%

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras

Xiao

Chen

et al. 2018

Sensors

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The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones’ position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.

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A multi-pronged approach for indoor positioning with WiFi, magnetic and cellular signals

Cited by 26 publications

References 4 publications

Review of Indoor Positioning: Radio Wave Technology

Review of Indoor Positioning: Radio Wave Technology

Sight-to-Sound Human-Machine Interface for Guiding and Navigating Visually Impaired People

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras

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