Indoor localization without the pain

By Krishna Chintalapudi, Anand Padmanabha Iyer, V. Padmanabhan. ACM/IEEE International Conference on Mobile Computing and Networking, 2010

1,047 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1ab6584dd8d61b3790536d4c6aaee1119f30ecdb

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In Electrical and Electronic Engineering

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

In Ocean Engineering

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

In Signal Processing

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2. Zheng Yang
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4. Shiwen Mao
5. Jie Xiong
6. Xuyu Wang
7. S. Chan
8. Suining He

Citations in the corpus

• SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “However the best known systems in this space tend to achieve a median accuracy of around 2–4 m [3, 4], and the 80 th percentile error is often as high as 5 m due to insufficient modeling of RSSI which in practice depends not only on the location of the target but also on the changing environment.”
• Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications (2017, background): “Hence, the work in [50] proposed to use the current WLAN infrastructure and Access Points (Aps) without pre-deployment calibration and a genetic algorithm for energy-efficient localization purposes, like WLAN access points (APs).”
• 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].”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, background): “For example, EZ [38] is a configuration-free local-”
• A reliable and accurate indoor localization method using phone inertial sensors (2012, methodology): “The EZ algorithm [3] computes both the receiver as well as the AP positions, along with parameters of the propagation model, by solving a set of simultaneous nonlinear equations.”
• Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges (2015, background): “Although low-cost, WiFi-based indoor localization offers lower accuracy (refer to [148] for overview), and com-”
• 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].”
• Accurate indoor localization with zero start-up cost (2014, background): “), or, 2) it needs exhaustive fingerprinting of the environment to learn the spatial distribution of signal strength, either in a training phase [28, 18] or via crowdsourcing [26, 5] to achieve meter accuracy at best.”
• Push the limit of WiFi based localization for smartphones (2012, methodology): “Methods in prior work mainly focus on employing more sensing modalities, less infrastructure support, and reducing the efforts of building signal maps [2,8,15,24].”
• Epsilon: A Visible Light Based Positioning System (2014, methodology): “(1) The deployment of illumination lights is much (over one order of magnitude) denser than that of WiFi access points (APs).”
• Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings (2013, methodology): “The accuracy of indoor localization could be improved with fingerprinting algorithms at the cost of significant manual effort [50] or with use of compute intensive algorithms [20].”
• Multi-Person Localization via RF Body Reflections (2015, background): “Over the past decade, the networking community has made major advances in RF-based indoor localization [5, 34, 21, 26, 38, 17, 21, 11, 22], which led to systems that can localize a wireless device with centimeter-scale accuracy.”
• ArrayTrack: A Fine-Grained Indoor Location System (2011, neutral): “This allows ArrayTrack to determine a client’s location to within centimeters in real time, something not possible with model- or map-based approaches, which build up information by averaging over relatively long time windows, resulting in median location errors between two and four meters [7].”
• WiFi-based indoor positioning (2015, background): “By considering the cost in terms of deploying anchor nodes as well as the coverage area, the most possible solution is expected in WiFi [9] systems.”
• Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, methodology): “, EZ [3] and Radar [1]), and that the fusion with WiFi leads to a 90 percentile accuracy of 3.”
• A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, background): “The key idea of the EZ localization system is that the RSS observations, less with GPS location at the building entrance or near a window and most from unknown indoor locations, are constrained by the physics of wireless propagation [92].”
• Wireless Sensing for Human Activity: A Survey (2020, background): “, GPS [75], [76], infra-red (IR) motion sensor [74]) to enhance the stability of indoor localization.”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “Received Signal Strength (RSS) is the most commonly used signal feature for WiFi-based indoor localization [Bahl and Padmanabhan 2000; Youssef and Agrawala 2005; Chintalapudi et al. 2010; Park et al. 2010; Goswami et al. 2011].”
• UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, methodology): “In [8], authors categorised approaches according to the technique used for localization into several paradigms, including calibration-free localization [9], WLAN based techniques [10], Dead-reckoning [11], simultaneous localization and mapping (SLAM) [12] and multi-modal sensing [13].”