The trip to WiFi indoor localization across a decade — A systematic review

Qiao, Shuang; Cao, Chenhong; Zhou, Haoquan; Gong, Wei

doi:10.1109/cscwd57460.2023.10152700

Cited by 1 publication

(2 citation statements)

References 15 publications

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“…With the rising demand for indoor location-based services, indoor positioning techniques have been proposed, including geomagnetic [1], ultra-wideband [2], Bluetooth [3], Wi-Fi [4], audio [5], visible light [6], and computer vision [7]. Wi-Fi Received Signal Strength (RSS) is highly susceptible to transient power from the transmitter and indoor environments, causing substantial fluctuations and compromising stable detection performance [8,9]. Wi-Fi, supporting the IEEE 802.11n standard, now incorporates Multiple-Input Multiple-Output (MIMO) technology.…”

Section: Introductionmentioning

confidence: 99%

“…Faced with the complex multipath environment indoors, conventional channel attenuation models struggle to accurately describe the relationship between signal characteristics and distance [15]. Additionally, while ranging models based on Time of Flight (TOF), Time Difference of Arrival (TDOA), and Round Trip Time (RTT) have their advantages, they often face challenges with clock synchronization and clock drift [4,9]. Despite the introduction of some countermeasures, they often have higher computational complexity compared to fingerprint localization [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization

Xie,

Yu,

Guo

et al. 2023

Applied Sciences

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In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods.

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