Real-Time Indoor Positioning System Based on RFID Heron-Bilateration Location Estimation and IMU Inertial-Navigation Location Estimation

Ho, Chian C.; Lee, River

doi:10.1109/compsac.2015.317

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…In [10], the Monte Carlo approach is used to provide a range-based approach. In [15], a real-time indoor positioning system is proposed with RFID Heron-bilateration location estimation and inertial-navigation location estimation, based on internal inertial measurement unit module. In [10], a target localisation algorithm is proposed based on time detection and a voting mechanism.…”

Section: Related Workmentioning

confidence: 99%

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Wearable indoor localisation approach in Internet of Things

Huang

Zhou

et al. 2016

IET Networks

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Localisation is an essential and important research issue in Internet of Things (IoT). Most localisation schemes focus on outdoor IoT. However, indoor IoT is required in some applications such as target detection and tracking, and emerging smarter healthcare. As such, localisation approaches designed for indoor IoT are necessary. Note that the smarter healthcare emerges at an unprecedented speed while few localisation schemes can be directly exploited for them with accuracy guarantee. In this study, the authors propose a wearable indoor localisation approach to improve the localisation accuracy of previous work. In this approach, the wearable devices embedded into person are used to realise their displacement vector in mobile environments, thus estimating their own locations. In addition, the authors propose a walking prediction mechanism to increase localisation accuracy. The real-world experiment results demonstrate that the proposed approach achieves lower localisation error in various moving speeds.

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Section: Related Workmentioning

confidence: 99%

“…Over the last few decades, many localisation approaches for IoT including sensor networks have been proposed to provide location information [1–23]. Generally speaking, these schemes are of two categories: range‐based and range‐free.…”

Section: Related Workmentioning

confidence: 99%

Wearable indoor localisation approach in Internet of Things

Huang

Zhou

et al. 2016

IET Networks

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“…The traditional data acquisition process separate the position and fingerprint data, which is the main reason leading to a large amount of data acquisition workload. By contrast, the inertial navigation (IN) technology [9,10] effectively realizes the dynamic fingerprint data acquisition by recording the fingerprint data and position simultaneously, which greatly improves the work efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

Duan¹,

Hu²,

Feng³

et al. 2018

Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)

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The demand of indoor positioning become more and more urgent with popularity of the smart home, smart medical, etc. In recent years, the research on indoor positioning has achieved some results, especially the method based on Wi-Fi, which is due to Wi-Fi technology is mature, Wi-Fi signal is relatively stable and has a wide range of coverage, etc. Thus, fingerprint based Wi-Fi indoor positioning is low cost, easy to acquire data and implement. However, there are still many problems in the practical application even though the theoretical research is mature. In this paper, we proposed a fast fingerprint data acquisition method based on dense sampling and interpolation aim at the problem of fingerprint database establishment, which acquire data efficiently and take into account maintenance of fingerprint database later.

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“…Furthermore, the studies by Jiménez Ruiz, Seco Granja, Prieto Honorato, and Guevara Rosas (2012), Ho and Lee (2015) and Tian, Salcic, Wang, and Pan (2015) made use of RFID and Inertial while the studies by Hasani, Talvitie, Sydanheimo, Lohan, and Ukkonen (2015) and Ettlinger and Retscher (2016) made use of RFID and WLAN. Similarly, the studies by De Silva, Mann, and Gosine (2015) and Marin-Garcia, Chavez-Burbano, Muñoz-Arcentles, Calero-Bravo, and Perez-Jimenez (2015) used a combination of Ultrasound and Optical, while Sosa-Sesma and Perez-Navarro (2016) used Ultrasound and WLAN.…”

Section: Hybrid Positioning Systemsmentioning

confidence: 99%

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

149

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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Real-Time Indoor Positioning System Based on RFID Heron-Bilateration Location Estimation and IMU Inertial-Navigation Location Estimation

Cited by 14 publications

References 8 publications

Wearable indoor localisation approach in Internet of Things

Wearable indoor localisation approach in Internet of Things

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

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