Proceedings ArticleDOI
SpotFi: Decimeter Level Localization Using WiFi
Manikanta Kotaru,Kiran Joshi,Dinesh Bharadia,Sachin Katti +3 more
- Vol. 45, Iss: 4, pp 269-282
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TLDR
SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems.Abstract:
This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.read more
Citations
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Proceedings ArticleDOI
Two beams are better than one: towards reliable and high throughput mmWave links
TL;DR: In this paper, a reliable mm-wave system that implements multi-beamforming and user tracking to handle environmental vulnerabilities is proposed, which creates constructive multibeam patterns and optimizes their angle, phase, and amplitude to maximize the signal strength at the receiver.
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Landmark Graph-Based Indoor Localization
TL;DR: A novel, low-cost, high-accuracy indoor localization method based on a landmark graph is proposed and the experimental results show that the proposed method outperforms the state-of-the-art methods.
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HiQuadLoc: A RSS Fingerprinting Based Indoor Localization System for Quadrotors
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CloudNavi: Toward Ubiquitous Indoor Navigation Service with 3D Point Clouds
TL;DR: CloudNavi is presented, a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device to accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm.
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Precise and accurate wireless signal strength mappings using Gaussian processes and path loss models
TL;DR: A new modeling approach is presented that generates precise (low variance) and accurate (low mean error) wireless signal strength mappings in robot localization by incorporating path loss models, which are parametric functions that although lacking in accuracy, generalize well.
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