Integration of UWB RSS to Wi-Fi RSS fingerprinting-based indoor positioning system

Chong, Alvin-Ming-Song; Yeo, Boon-Chin; Lim, W. L.

doi:10.1080/23311916.2022.2087364

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…As a result, several strategies utilizing various wireless technologies have been investigated. The mentioned technologies encompass the IEEE 802.11 standard (commonly known as Wi-Fi), Bluetooth Low Energy (BLE), Zigbee, ultra-wideband (UWB), as well as other combinations of these technologies [3] [4]. Several positioning techniques have also been considered, including the time difference of arrival (TDoA), angle of arrival (AoA), lateration, and pattern matching such as fingerprinting [5].…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Indoor Localization with Movement Validation and Fingerprinting Technique under IEEE 802.15.4 Network

Puspitaningayu,

Funabiki,

Huo

et al. 2024

IJCDS

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The study of indoor localization has been extensively studied, either in terms of wireless technologies or localization techniques. The accuracy is then challenged when the monitored object is actively moving. Previously, we studied a continuous and low-power fingerprint-based indoor localization system using IEEE 802.15.4 (FILS15.4), which has been integrated into a smart environmental IoT platform. Although fingerprint-based localization offers a great advantage in its simplicity, it relies on real-time signal strength measurements and databases. Thus, it suffers challenges in accuracy when the object is continuously moving. In this study, we focus on developing dynamic positioning, where users continuously move from one room to another. Due to human movements, the fluctuation of the link quality indicator (LQI) can affect the detection accuracy. To avoid false detection, we propose a movement validation method by checking the variance of the LQI and accelerometer to differentiate the cause of fluctuations and increase the detection accuracy. For experiments, we run the test-bed of FILS15.4 on a two-floor layout. Five to six receivers were allocated to detect multiple users. The results show that the system yields 96.2% accuracy using six receivers simultaneously. Thus, it gives sufficient detection accuracy even for dynamic conditions.

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Section: Introductionmentioning

confidence: 99%

“…Consequently, rather than using location coordinates, limiting the detection resolution to one room is preferable in FILS15. 4. Subsequently, the addition of assigned receivers and fingerprints for each room enhanced the precision of detection in two distinct configurations.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Indoor Localization with Movement Validation and Fingerprinting Technique under IEEE 802.15.4 Network

Puspitaningayu,

Funabiki,

Huo

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…There were methods that used UWB signals to locate sensors by measuring RSS [43] in an emission source localization problem. A combined approach complementing WiFi RSS fingerprinting with UWB RSS measurements and using a neural network was proposed in [44]. This approach allowed the authors to improve the positioning accuracy to 0.66 m.…”

Section: Introductionmentioning

confidence: 99%

Measuring Received Signal Strength of UWB Chaotic Radio Pulses for Ranging and Positioning

Efremova,

Kuzmin,

Itskov

2023

Electronics

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The use of ultra-wideband (UWB) signals for local positioning is very attractive for practice, because such signals have the potential to provide centimeter precision. In this paper, we consider wireless ranging (distance measurement) and positioning, using one of the kinds of UWB signals, i.e., chaotic radio pulses, which are noise-like signals with no constant shape. The distance measurement is based on an assessment in the receiver of the power of UWB chaotic radio pulses emitted by the transmitter. A new method for estimating their power and its experimental implementation is proposed and described. Experimental layouts of the transmitter and receiver and the principles of their operation are described. To determine the main features of this method under real signal propagation conditions, full-scale indoor measurements were carried out, and statistical estimates of the accuracy were made. We present the results of experimental testing of the proposed approach for positioning the emitter relative to a system of anchors in an office space 6 × 6.5 m2 in the mode of measuring object coordinates on a line and on a plane. The mean absolute error (MAE) of distance measurement (1D) was 25 cm, and the root mean squared error (RMSE) was 39 cm. When positioning on a plane (2D), the MAE of coordinate estimation was 34 cm and the RMSE was 42 cm. The proposed distance measurement method is intended for use in wireless UWB transceivers used in wireless sensor networks.

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“…A positioning technique to utilise the medium for target position estimation is required. One of the popular positioning techniques used with Wi-Fi is fingerprinting positioning technique with Received Signal Strength (RSS) as the distance-dependent parameter [3].…”

Section: Introductionmentioning

confidence: 99%

Zone-Based Indoor Positioning System in Faculty Building with Neural Networks

Yeo

Chong

Lim

et al. 2022

IJORAS

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Pervasive Wi-Fi deployment has made Wi-Fi an economically convenient wireless platform for developing an Indoor Positioning System (IPS). This paper presents a zone-based IPS developed on Wi-Fi using fingerprinting technique with Probabilistic Neural Network (PNN) and Radial Basis Function Neural Network (RBFNN) to predict target positions. The zone-based IPS is deployed in an indoor environment (a faculty building) with four Wi-Fi modules separately placed. The indoor environment consists of office rooms and laboratories separated by concrete walls. A two-dimensional coordinate system and zone label are deployed to define each location. After that, data collection is performed on each location. The Wi-Fi Received Signal Strength (RSS) for every Wi-Fi module at each location is discovered, labelled with the location coordinate and zone value to form a fingerprint and finally stored in a database. Fingerprints in the database are then separated into training and testing sets for training and testing of PNN and RBFNN. The testing result shows that the mean positioning error for coordinate prediction of PNN and RBFNN is 3.84m and 6.91m, respectively. Although RBFNN has a large mean positioning error, RBFNN presented a zone positioning accuracy of 78.7%, which is close to the 82.2% accuracy presented by PNN.

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Integration of UWB RSS to Wi-Fi RSS fingerprinting-based indoor positioning system

Cited by 5 publications

References 28 publications

A Dynamic Indoor Localization with Movement Validation and Fingerprinting Technique under IEEE 802.15.4 Network

A Dynamic Indoor Localization with Movement Validation and Fingerprinting Technique under IEEE 802.15.4 Network

Measuring Received Signal Strength of UWB Chaotic Radio Pulses for Ranging and Positioning

Zone-Based Indoor Positioning System in Faculty Building with Neural Networks

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