DeepNar: Robust Time-based Sub-meter Indoor Localization using Deep Learning

Hashem, Omar; Harras, Khaled A.; Youssef, Moustafa

doi:10.1109/secon48991.2020.9158428

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…[17] presents a particle filter-based RTT localization using the Gaussian mixture model. DeepNar [11] develops an end-toend neural network that maps the RTT measurement from multiple APs to the probabilities at different locations and then uses the probability as the weight to get the final estimation.…”

Section: B Localization Using Wifi Rttmentioning

confidence: 99%

“…For synchronization, one antenna is shared by both cards as a common reference such that there are 5 working antennas on the AP. DeepFi [21] trained a neural network similar to DeepNar [11] that maps the CSI data to location probability. To improve performance, [6] hops over different WiFi channels to form a wide-band sensing and shows a centimetre-level accuracy.…”

Section: Localization Using Wifi Cfrmentioning

confidence: 99%

“…Additionally, the trajectory of the STA is planned as a circle whose radius is 3m, and the linear velocity is 0.94m/s. The propagation model is shown in equation (11), which consists of a power loss according to [29] and a phase shift due to the time delay caused by propagation. The final CSI collected would be H sim = i H simi and then the RTCSI can be calculated as H 2 sim .…”

Section: A Multi-path Effect On Rtpmentioning

confidence: 99%

See 2 more Smart Citations

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Shi,

Li,

Tang

et al. 2024

IEEE J. Select. Areas Commun.

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Indoor localization using WiFi signals has been studied since the emergence of WiFi communication. This paper presents a novel training-free approach to indoor localization using a customized WiFi protocol for data collection and a factor graph-based back-end for localization. The protocol measures the round-trip phase, which is very sensitive to small changes in displacement. This is because the sub-wavelength displacements introduce significant phase changes in WiFi signal. However, the phase cannot provide absolute range information due to angle wrap. Consequently, it can only be used for relative distance (displacement) measurement. By tracking the round-trip phase over time and unwrapping it, a relative distance measurement can be realized and achieve a mean absolute error (MAE) of 0.06m. For 2-D localization, factor graph optimization is applied to the round-trip phase measurements between the STA (station) and four APs (access points). Experiments show the proposed concept can offer a decimeter-level (0.26m MAE and 0.24m 50%CDF) performance for real-world indoor localization.

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Section: B Localization Using Wifi Rttmentioning

confidence: 99%

Section: Localization Using Wifi Cfrmentioning

confidence: 99%

Section: A Multi-path Effect On Rtpmentioning

confidence: 99%

See 1 more Smart Citation

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Shi,

Li,

Tang

et al. 2024

IEEE J. Select. Areas Commun.

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“…In DeepNar [19] the positioning is estimated from Wi-Fi FTM RTT fingerprint through a fully connected neural network, yielding sub-meter (0.75m) localization precision. In [20] a deep long short term memory (LSTM) neural network is applied to encode temporal dependencies upon RSSI fingerprint towards positioning, yielding meter-level (1.5m) localization precision.…”

Section: Related Workmentioning

confidence: 99%

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Chan

Lai

et al. 2022

IEEE Access

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This paper presents a multi-source fusion smartphone localization solution using Wi-Fi Fine Time Measurement (FTM) and Pedestrian Dead Reckoning (PDR), calibrated via multi-source and unsupervised crowdsourcing. In crowdsourcing phase, user movement within the site utilizes PDR to infer their location, and this location is used to calibrate the FTM data. The multi-layer perceptron (MLP) of the ranging model is suitable for non-line-of-sight (NLOS) reception, and the ranging accuracy is improved by more than 24%. In the positioning phase, the 90 percentile error of the ranging model trained using only crowdsourced data is less than 1.37m, which is 32% smaller than the traditional weighted least squares (WLS) localization error.

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“…In [14], DeepNar was proposed as a neural network-based fingerprinting method. Authors have used Wi-Fi Round Trip Time (RTT) fingerprints from 7 BSs to localize a UE in an indoor scenario.…”

Section: Related Workmentioning

confidence: 99%

A CNN Approach for 5G mm Wave Positioning Using Beamformed CSI Measurements

Kia

Ruotsalainen

Talvitie

2022

2022 International Conference on Localization and GNSS (ICL-GNSS)

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The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (CSI). By observing CSI, it is possible to characterize the multipath channel between the transmitter and the receiver, and thus provide a good source of spatiotemporal data to find the position of a User Equipment (UE). We collect ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals from one Base Station (BS) is collected at the reference points with known positions to train a CNN. We evaluate our work by testing: a) the robustness of the trained network for estimating the positions for the new measurements on the same reference points and b) the accuracy of the CNN-based position estimation while the UE is on points other than the reference points. The results prove that our trained network for a specific urban environment can estimate the UE position with a minimum mean error of 0.98 m.

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DeepNar: Robust Time-based Sub-meter Indoor Localization using Deep Learning

Cited by 11 publications

References 26 publications

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

A CNN Approach for 5G mm Wave Positioning Using Beamformed CSI Measurements

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