A Dynamic Weighted Trilateration Algorithm for Indoor Localization Using Dual-Band WiFi

Mathivannan, S.; Srinath, S.; Shashank, R.; Aravindh, R.; Balasubramanian, Vidhya

doi:10.1007/978-3-030-17246-6_14

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…This challenge can be tackled by recasting it as a minimization problem that can be addressed mathematically to obtain the best likelihood. Mathivannan et al [10] proposed a novel calibration-free, adaptive, weighted trilateration technique that uses the characteristics of dual-band Wi-Fi, and an iterative heuristic approach to estimate the most likely position of a smart phone. To improve the time complexity of this technique, additional optimization techniques are implemented.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint-based indoor positioning system using BLE: real deployment study

Safwat

Shaaban²,

Al-Tabbakh³

et al. 2023

Bulletin EEI

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There are a myriad of applications where the localization of interior surroundings is vital in the era of smart cities Bluetooth low energy (BLE) technology is designed for short-range wireless communication, low energy consumption, low cost hardware design and simple deployment with respect to other technologies. This paper presents a low cost BLE fingerprint-based indoor positioning system, where a minimum number of Beacons are deployed in different test bed subareas with different conditions. Collected measured received signal strength indicator (RSSI) signals received from all beacons in each grid cell of all areas of interest are stored. We experimented two deterministic matching algorithms: k-nearest neighbors (KNN) and weighted algorithm (WKNN), to match previously collected RSSI readings with the RSSI at mobile unknown location, to determine where the user is. Experiments results show that WKNN algorithm manages to obtain less mean and standard deviation positioning error for all subareas, that experiencing different conditions of obstructions, reflections, and interferences.

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Section: Related Workmentioning

confidence: 99%

Fingerprint-based indoor positioning system using BLE: real deployment study

Safwat

Shaaban²,

Al-Tabbakh³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Because Wi-Fi signals are readily available in public indoor environments, Wi-Fi-based indoor positioning methods are the most popular [ 21 ]. Currently, the commonly used Wi-Fi positioning models are the trilateral method [ 22 ] and fingerprint method [ 23 ]. The Wi-Fi fingerprint is composed of the Wi-Fi received signal strength indicator (RSSI) of different APs for known location reference points (RPs).…”

Section: Related Workmentioning

confidence: 99%

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints

Zhang

2021

Sensors

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Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.

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“…The localization methods like fingerprinting [ 47 , 48 ] and trilateration [ 49 ] can be realized using Wi-Fi. Similarly, signal measurement principles like RSS [ 50 ], CSI [ 51 ], TOA [ 52 ], and their hybrid combination [ 53 ] can be used to provide Wi-Fi-based localization service.…”

Section: Wireless Technologies For Ipsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

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In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

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A Dynamic Weighted Trilateration Algorithm for Indoor Localization Using Dual-Band WiFi

Cited by 8 publications

References 15 publications

Fingerprint-based indoor positioning system using BLE: real deployment study

Fingerprint-based indoor positioning system using BLE: real deployment study

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

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