SpotFi: Decimeter Level Localization Using WiFi

By Manikanta Kotaru, Kiran Raj Joshi, Dinesh Bharadia, Sachin Rajsekhar Katti. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2015

1,471 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/347c759f37d3c10ecee51dbcc80572d06ec70fe8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Yunhao Liu
2. Zheng Yang
3. Kun Qian
4. Chenshu Wu
5. Gang Zhou
6. Kyle Jamieson
7. Zimu Zhou
8. Wen Wang

In Ocean Engineering

1. Zheng Yang
2. Yunhao Liu
3. Zimu Zhou
4. Chenshu Wu
5. Kun Qian
6. Kyle Jamieson
7. Lionel Ming-shuan Ni
8. Jie Xiong

In Signal Processing

1. Kun Qian
2. Yunhao Liu
3. Zheng Yang
4. Chenshu Wu
5. Kyle Jamieson
6. Gang Zhou
7. Hang Zhao
8. Jie Xiong

Citations in the corpus

• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, background): “SpotFi [7] jointly estimates AoA and ToF of the incident signal by synthesising sensors at sub-carrier level.”
• Decimeter-Level Localization with a Single WiFi Access Point (2016, methodology): “State-of-theart systems have achieved an accuracy of tens of centimeters, even using commodityWiFi chipsets [30, 32, 18].”
• A Survey of Indoor Localization Systems and Technologies (2017, methodology): “[23] propose SpotFi that uses CSI and RSSI to obtain an accurate estimate of AoA and ToF, which are used to obtain user location.”
• RF-based 3D skeletons (2018, background): “Novel algorithms have led to accurate localization within tens of centimeters [19, 34].”
• Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “With 802.11n and 802.11ac standards, more antennas are equipped at a single AP and this opportunity is leveraged to obtain the AoA information for localization [19, 47].”
• WiFi Sensing with Channel State Information (2019, methodology): “SpotFi [46] NR: Sampling Time Offset; SE: Signal Isolation, Multiple Packets and Transmitters M: AoA, ToF, MUSIC, CSI Smoothing, Gaussian Mean Clustering Device-based Localization Median Localization Accuracy: 40cm”
• Precise Power Delay Profiling with Commodity Wi-Fi (2015, background): “site survey, and no sophisticated AP hardware [1, 6, 9, 47, 48].”
• LiFS: low human-effort, device-free localization with fine-grained subcarrier information (2016, background): “Most current localization systems [14, 25, 44, 42], however, require the person to carry a device (such as a mobile phone), making them a poor fit for some applications.”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “variations in the phase of CSI [16], [32].”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “SpotFi [Kotaru et al. 2015] adopts superresolution AOA estimation algorithms and proposes a set of filtering and estimation techniques to derive the LOS AOA using commercial WiFi infrastructure.”
• Through-Wall Human Pose Estimation Using Radio Signals (2018, background): “For example, one can track a person using the WiFi signal from their cellphone [44, 24, 40].”
• Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “More recently, other approaches rely on angle-of-arrival [87], now built-in in Bluetooth 5.”
• Towards 3D human pose construction using wifi (2020, background): “…number of investigations are conducted to make use of WiFi devices to enable various sensing tasks, including indoor localization and tracking [5, 22, 25, 33, 39, 42, 48–50, 52, 53, 65], walking speed estimation [17, 43], breathing and heartbeats inference [6, 55, 58, 61], human identification…”
• mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, methodology): “e ability of different parameters to resolve signals. Head-to-head comparisons in parameter estimation and passive localization demonstrate 3.5× accuracy improvements over the state-of-the-art SpotFi [19] system. Further experiments measure the effect of adding another signal dimension (Doppler shif”