Data Rate Fingerprinting: A WLAN-Based Indoor Positioning Technique for Passive Localization

Duan, Yaoxin; Lam, Kam-Yiu; Lee, Victor C. S.; Nie, Wendi; Li, Kai; Li, Hao

doi:10.1109/jsen.2019.2911690

Cited by 42 publications

(27 citation statements)

References 30 publications

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“…The RSSI-based technology realizes localization by characterizing the energy attenuation of wireless signals during propagation, which is readily available from commodity WiFi. Several methods combine the RSSI readings from multiple access points (APs) with propagation models and then locate the persons through triangulation [18] or fingerprint collection [19], [20]. However, since RSSI is a coarse characterization of the channel, reliable RSSI-based positioning systems tend to achieve only room-level accuracy.…”

Section: A Passive Indoor Localizationmentioning

confidence: 99%

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.

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Section: A Passive Indoor Localizationmentioning

confidence: 99%

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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“…For higher resolution, larger localization area and more users, passive localization with mobile devices is preferred. Since the data rates at different locations are different, Duan et al [19] exploits data rate information of all available APs as the fingerprints for localization. In order to improve the resolution, their subsequent work [7] includes other fingerprints such as packet delivery ratio (PDR) for localization, which improves the average accuracy to 2.5 m.…”

Section: Phone State Decisionmentioning

confidence: 99%

“…If the WiFi device used in the training phase is different from the actual device used in the testing phase, the localization accuracy could be significantly reduced [7]. To mitigate this problem, one may use special fingerprints such as data rate (DR) [19], RSS ratio [34], RSSI difference [35] to avoid heterogeneity. However, due to power control on the mobile devices, the number of frames or power sent from a mobile device may vary significantly, which makes all of those fingerprints less reliable.…”

Section: Device Heterogeneitymentioning

confidence: 99%

Passive Indoor Localization with WiFi Fingerprints

Hoang¹,

Yuen²,

Ren³

et al. 2021

Preprint

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This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of ∼0.8 m when the phone is actively transmitting data frames and ∼1.5 m when it is not transmitting data frames.

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“…As a state-of-the-art technology, Wi-Fi fingerprinting indoor localization systems (IPS) have been extensively researched for both localization [ 6 , 7 ] and activity recognition [ 8 , 9 ] applications. As the radio frequency (RF) characteristics of Wi-Fi signals at each location are unique due to their different propagation paths, the RF characteristics can be considered unique fingerprints.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Zhang

Lee

Choi

2021

Sensors

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In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

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Data Rate Fingerprinting: A WLAN-Based Indoor Positioning Technique for Passive Localization

Cited by 42 publications

References 30 publications

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Passive Indoor Localization with WiFi Fingerprints

Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

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