Decimeter Level Indoor Localization Using WiFi Channel State Information

Yang, Runming; Yang, Xiaolong; Wang, Jiacheng; Zhou, Mu; Tian, Zengshan; Ling-xia, LI

doi:10.1109/jsen.2021.3067144

Cited by 22 publications

(9 citation statements)

References 33 publications

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“…Here, CSI is expressed through M receiving antennas and N equidistant subcarriers [15]. Each element of this matrix is a linear combination of multipath components, as presented in Equation 3.…”

Section: A Channel State Informationmentioning

confidence: 99%

“…Advancements in the extraction of channel state information from common off-the-shelf devices have enabled higher accuracy localization methods that extract novel features from raw data [11]- [14]. Meter-level and even decimeter-level localization accuracy have been demonstrated within complex environments [3], [4], [15]. Compared to purely algorithmic methods such as multilateration, CSI has been used to extract uniquely identifying features for fingerprinting (Section III).…”

Section: Introductionmentioning

confidence: 99%

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MobLoc: CSI-Based Location Fingerprinting With MUSIC

Mazokha,

Bao,

Sklivanitis

et al. 2023

J. Ind. Sea. Pos. Nav.

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Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest-achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in realworld environments.In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudo-spectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc [1], where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods.The results demonstrate that our method outperforms SpotFi [2], EntLoc [3], and AngLoc [4] and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33m median (and 0.82m, 90th percentile) localization error in a simple environment and 1.15m median (2.59m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc accuracy, we consider its performance as a trade-off for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.

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Section: A Channel State Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MobLoc: CSI-Based Location Fingerprinting With MUSIC

Mazokha,

Bao,

Sklivanitis

et al. 2023

J. Ind. Sea. Pos. Nav.

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“…MIMO-enabled Wi-Fi devices can estimate AoA using the phase difference between individual elements of the antenna array. The authors of [21] utilised the Wi-Fi MIMO antenna systems to provide a higher-dimensional location signature, as well as to showcase the spatial diversity of CSI, resulting in an accuracy of 0.95 m. With two MIMO-enabled APs in a complex small laboratory environment, the AoA estimation accuracy achieved 5.82 • with a localisation accuracy of 0.66 m [24]. Furthermore, a COTS Wi-Fi device with MIMO antennas could provide an error of at most 2.5 • for AoA and AoD when estimating the orientation of an MIMO-enabled Wi-Fi DUT using joint estimation of AoA and AoD and phase correction [25].…”

Section: Related Workmentioning

confidence: 99%

Physical Tampering Detection Using Single COTS Wi-Fi Endpoint

Chan

Lai

et al. 2021

Sensors

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This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

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“…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]. Fingerprint-based localization methods have received widespread attention due to their simple deployment and effective positioning results [4].…”

Section: Introductionmentioning

confidence: 99%

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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Decimeter Level Indoor Localization Using WiFi Channel State Information

Cited by 22 publications

References 33 publications

MobLoc: CSI-Based Location Fingerprinting With MUSIC

MobLoc: CSI-Based Location Fingerprinting With MUSIC

Physical Tampering Detection Using Single COTS Wi-Fi Endpoint

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

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