You are facing the Mona Lisa: spot localization using PHY layer information

By Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2012

563 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/81493baa7e7e62d4e83d80ba667fcc82af6dc20f

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In Computer Networks and Communications

1. Alex X. Liu
2. Yunhao Liu
3. Zheng Yang
4. Chenshu Wu
5. Wen Wang
6. Kun Qian
7. Muhammad Shahzad
8. Guidong Zhang

In Electrical and Electronic Engineering

1. Yunhao Liu
2. Zheng Yang
3. Chenshu Wu
4. Shiwen Mao
5. Xuyu Wang
6. Kun Qian
7. Lingjun Gao
8. Zimu Zhou

In Ocean Engineering

1. Shiwen Mao
2. Xuyu Wang
3. Lingjun Gao
4. Zheng Yang
5. Yunhao Liu
6. Chenshu Wu
7. Zimu Zhou
8. Jie Xiong

Citations in the corpus

• Precise Power Delay Profiling with Commodity Wi-Fi (2015, background): “which can makes them a good choice for the fingerprintbased localization design [45], [46].”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, methodology): “For example, FIFS [12] and PinLoc [13] use CSI obtained through the off-theshelf IWL 5300 NIC to build reliable fingerprints.”
• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “Various signal features, from RSSI [27] to CSI [13], have been exploited as location fingerprints.”
• Electronic frog eye: Counting crowd using WiFi (2014, methodology): “Recently, Channel State Information (CSI) was used for localization [22], [23], [24].”
• Accurate indoor localization with zero start-up cost (2014, background): “The second mandates signal fingerprinting [3, 27, 39] either in a training phase or via crowdsourcing [26, 38, 18].”
• Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, methodology): “Furthermore, the phase sanitization method introduced in [20] could not work for our case because the phase sanitization process also removes the phase shifts caused by body movements.”
• We Can Hear You with Wi-Fi! (2014, background): “1Jaws and tongue movement based lip reading can only recognize 30%∼40% of the whole vocabulary of English [19] Popular vision-based approaches include Xbox Kinect [2] and Leap Motion [1], which use RGB hybrid cameras and depth sensing for gesture recognition.”
• From RSSI to CSI (2013, methodology): “Recent works have reported 1.2m and 1.8m with probabilities of 50% and 90%, respectively, for CSI-assisted ranging [Wu et al. 2012], and mean accuracy of 89% for 1m × 1m boxes by CSI-based fingerprinting [Sen et al. 2012b].”
• Phaser: enabling phased array signal processing on commodity WiFi access points (2014, background): “PinLoc [23] places the mobile in one of several pre-defined, geographically separated spots by statistical clustering and classification of 802.”
• CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “FIFS [10] and PinLoc [23] systems utilize CSI amplitude values extracted from off-the-shelf IWL 5300 NIC as fingerprints to estimate mobile user position.”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “Likewise, Sen et al. [2012] develop a spot indoor fingerprinting system, PinLoc, using CSI and achieve an accuracy of 89% for 100 spots.”
• Locating in fingerprint space: wireless indoor localization with little human intervention (2012, methodology): “In two recent works, FM radio [5] and Channel Frequency Response [27] are explored to use as fingerprints.”
• DeepFi: Deep learning for indoor fingerprinting using channel state information (2015, methodology): “In addition, the PinLoc system also exploits CSI information, while considering 1× 1 m(2) spots for training data [10].”
• A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned (2015, background): “Note that the deployment of 10 dedicated WiFi access points created a bias in favor of all systems in the infrastructurefree category.”
• Smartphones Based Crowdsourcing for Indoor Localization (2015, methodology): “In two recent works, FM radio [5] and Channel Frequency Response [29] are explored to use as fingerprints.”
• Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, methodology): “To handle the noises, previous works proposed to apply linear fitting over multiple subcarriers for calibration [13, 25].”
• SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “We note that the Algorithm 1 is similar to the data sanitization process in PinLoc [15] and is an extension of the process to multiple antennas.”