Avoiding multipath to revive inbuilding WiFi localization

By Souvik Sen, Jeongkeun Lee, Kyu-Han Kim, Paul Congdon. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2013

419 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/a7016075b373b572b3ced9f6712f0d565c608c7c

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In Electrical and Electronic Engineering

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

In Ocean Engineering

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

Citations in the corpus

• mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “Recent progress in this area has explored many ways of extracting different parameters of the wireless signal, such as angle-of-arrival (AoA) and time-of-flight (ToF) [11, 29, 48] for localization and tracking.”
• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, background): “Incorporating inertial sensors embedded in mobile devices exposes a new way for localization tracking [12].”
• CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “By using off-the-shelf Atheros chipsets with three antennas, the CUPID system [30] can obtain CSI to estimate AOA with the mean error about 20 degree based on MUSIC algorithm.”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, methodology): “The recently proposed CUPID system [43] adopts the off-the-shelf Atheros chipset with three antennas and can obtain CSI to estimate AOA, achieving a mean error of about 20◦ with the MUSIC algorithm.”
• WiFi-based indoor positioning (2015, background): “By having gyroscope sensors’ assistance from mobile phones, the multipath effect can be further mitigated [11].”
• Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, methodology): “Recently CSI has been used for human activity recognition [10,26,27,30,35] as well as indoor localization [19,32].”
• Wireless Sensing for Human Activity: A Survey (2020, background): “Furthermore, Sen et al. [87] propose to effectively reduce the impact of multipath reflections by applying CSI information to indoor localization system.”
• Keystroke Recognition Using WiFi Signals (2015, methodology): “CSI Based: CSI values obtained from COTS WiFI network interface cards (NICs) (such as Intel 5300 and Atheros 9390) have been recently proposed for activity recognition [6–10, 13] and localization [14–16].”
• Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges (2015, background): “plex multi-path cancellation techniques [149] are required to”
• We Can Hear You with Wi-Fi! (2014, methodology): “An IFFT (Inverse Fast Fourier Transformation) is first operated on the collected CSI to approximate the power delay profile in the time domain [36].”
• Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, background): “Generally, it requires objects to carry devices that transmits RF signals, and calculates signal parameters, e.g. AoA [9, 12, 23, 24, 39], ToF [29, 37, 39, 40], using fine-grained channel state information [43].”
• Precise Power Delay Profiling with Commodity Wi-Fi (2015, methodology): “which is one of the state-of-the-art indoor localization designs [28].”
• SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “All the major WiFi chip families (Broadcom, Atheros, Intel, and Mar-vell) expose quantized CSI per subcarrier per antenna [23].”
• Phaser: enabling phased array signal processing on commodity WiFi access points (2014, methodology): “Further work refines these methods to use a smartphone’s inertial sensors to estimate the line-of-sight path to the AP [22], or attempt to isolate the line-of-sight signal in order to decrease the complexity of angle-of-arrival estimation [12].”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, methodology): “Researchers have developed CSI based applications, including fall detection [11], presence detection [38], indoor localization [21], [34], and human crowd counting [31].”