The Horus WLAN location determination system

By Moustafa Youssef, Ashok K. Agrawala. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2005

1,719 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c0b2a6f62f9d3fd6e206bf89ec5ff89e60090168

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In Computer Networks and Communications

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

In Electrical and Electronic Engineering

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

In Ocean Engineering

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

Citations in the corpus

• Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, methodology): “ministic [2] and probabilistic methods [52], [53].”
• Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, background): “Indoor localization is an extensively studied topic, mostly relying on certain infrastructure, and WiFi is mostly explored [1–4, 19, 20, 32].”
• CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “For indoor localization accuracy, PhaseFi based on calibrated phases outperforms all the other schemes (i.e., FIFS, Horus, and ML) that are based on amplitudes.”
• A Survey of Positioning Systems Using Visible LED Lights (2018, methodology): “Fingerprinting Fingerprinting-based indoor positioning systems usually consist of two steps: i) off-line survey, and ii) on-line positioning [4, 47, 65-68].”
• 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].”
• The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, neutral): “In conventional fingerprinting, the online phase is used to compare the fingerprint obtained in real time with the stored ones and infer the location exploiting algorithms such as K nearest neighbors [137], Horus [138], or RADAR [135] for example.”
• Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “RADAR [2] is a pioneering system employing Wi-Fi RSSI information as a signature for localization and a lot of works are proposed later either to reduce the offline training load [5, 17] or improve the accuracy [49, 52].”
• Demo: Luxapose: indoor positioning with mobile phones and visible light (2014, background): “WiFi and other RF-based approaches deliver accuracies measured in meters and no orientation information, making them a poor fit for many applications like retail navigation and shelf-level advertising [1, 2, 10].”
• Indoor localization in multi-floor environments with reduced effort (2010, methodology): “In general, given sufficient labeled data in some environment, fingerprinting methods statistically learn the relations between the received signal strengths and the locations [3], [4].”
• A Survey of Indoor Localization Systems and Technologies (2017, background): “Youssef and Agrawala [38] propose Horus, which is a WiFi based localization system that relies on RSSI.”
• Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks (2009, background): “Methods belonging to this category differ among them because of the way in which the measurements in the online and offline phases are compared.”
• Recurrent Neural Networks for Accurate RSSI Indoor Localization (2019, methodology): “approaches, the probabilistic method is based on statistical inference between the target signal measurement and stored fingerprints using Bayes rule [7].”
• Push the limit of WiFi based localization for smartphones (2012, methodology): “Our investigation on these large errors reveals the insight that they are caused by possibly faraway locations with similar WiFi signatures, an intrinsic phenomenon of the radio signal propagation and fundamental limit of WiFi methods.”
• Avoiding multipath to revive inbuilding WiFi localization (2013, methodology): “Horus [3] proposes enhancements to RADAR by exploiting the structure of RSSI.”
• Challenges: device-free passive localization for wireless environments (2007, background): “Many systems over the years have tackled the localization problem including the GPS [3] system, infrared [13], ultrasonic [10], and radio frequency (RF) [2,8,16,21].”
• Decimeter-Level Localization with a Single WiFi Access Point (2016, background): “Chronos builds on vast literature on indoor WiFi-based localization [40, 13, 9, 51, 18, 48, 32, 30, 50, 4].”
• E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, methodology): “These systems localize a wireless emitter using Received Signal Strength (RSS) [6–8,11,17,22,38,41,43], OFDM channel state information [30], antenna arrays [14,37], RFID tags [34], rotating anchors [9], or visible LED lights [21].”
• RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “Another example of WLAN based tracking includes the " Horus WLAN location determination system " [8].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “Their toolbox yields much higher accuracy as compared to traditional methods, including including FIFS [487], Horus [488], and Maximum Likelihood [489].”
• 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.”