Accurate indoor localization with zero start-up cost
By Swarun Kumar, Stephanie Gil, Dina Katabi, Daniela Rus. ACM/IEEE International Conference on Mobile Computing and Networking, 2014
406 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/e5edfbdf645a3dbcdaf7d9fcbf350c67fbbadae5
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
- Jie Xiong
- Dina Katabi
- Kyle Jamieson
- Sachin Rajsekhar Katti
- Kiran Raj Joshi
- Kin Kwong Leung
- Faheem Zafari
- Athanasios Gkelias
In Ocean Engineering
- Jie Xiong
- Kin Kwong Leung
- Faheem Zafari
- Athanasios Gkelias
- Dinesh Bharadia
- Sachin Rajsekhar Katti
- Kiran Raj Joshi
- Manikanta Kotaru
Citations in the corpus
- A Survey of Indoor Localization Systems and Technologies (2017, background): “points for signal collection [21], basic localization systems (that can achieve reasonable localization accuracy) can be built without the need for additional infrastructure.”
- ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, background): “Ubicarse [19] takes this one step further by making the smartphone emulate a synthetic aperture radar through user induced motion.”
- SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “…WiFi deployments, although the estimated ToF values are different from the absolute ToFs, the joint estimation procedure provides AoA accuracy that is comparable to systems that require twice as many antennas [8] or non-standard configurations such as rotating antennas used in radar [6, 2].”
- Decimeter-Level Localization with a Single WiFi Access Point (2016, background): “State-of-theart systems have achieved an accuracy of tens of centimeters , even using commodity WiFi chipsets [30] [32] [18].”
- Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “may target at higher accuracy with information of human body and gesture recognition [196], [218].”
- mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “bining adjacent channels to form a larger bandwidth with channel hopping [19, 46, 50].”