FM-based indoor localization

By Yin Chen, Dimitrios Lymberopoulos, Jie Liu, Bodhi Priyantha. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2012

332 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/dcacdb4ecad39932c7d16a2be37e63265b043c7f

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. Chenshu Wu
4. Zimu Zhou
5. Kyle Jamieson
6. Lionel Ming-shuan Ni
7. Jie Xiong
8. S. Chan

In Ocean Engineering

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

In Signal Processing

1. Yunhao Liu
2. Zheng Yang
3. Chenshu Wu
4. Kyle Jamieson
5. S. Chan
6. Suining He
7. Jie Xiong
8. Zimu Zhou

Citations in the corpus

• Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, background): “WiFi [1]–[4], cellular [5]–[7], or even FM [8], [9] based approaches have shown great promise but may not be as effective when the signals are”
• SVELTE: Real-time intrusion detection in the Internet of Things (2013, methodology): “The location of a node can also be estimated in real-time using localization techniques [34].”
• Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “Such signals include Wi-Fi [2], Bluetooth [3], [4], FM radio [5], [6], radio-frequency identification (RFID) [7]–[10],”
• You are facing the Mona Lisa: spot localization using PHY layer information (2012, background): “Different schemes optimize distinct objectives, including accuracy [3–5], computation [4, 6], ease of calibration [7, 8], energy [9], etc.”
• Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “methods [21]–[23], ZigBee-based methods [24], [25], Frequency Modulation (FM) methods [26], [27], and IEEE 802.”
• Locating in fingerprint space: wireless indoor localization with little human intervention (2012, methodology): “In two recent works, FM radio [5] and Channel Frequency Response [27] are explored to use as fingerprints.”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “Chen et al. [2010] present an inquiry-based locating approach using Bluetooth RSS measurements.”
• Smartphones Based Crowdsourcing for Indoor Localization (2015, methodology): “In two recent works, FM radio [5] and Channel Frequency Response [29] are explored to use as fingerprints.”
• ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, methodology): “[7] leverage FM for indoor radio fingerprinting with infrastructure support, combining it with Wi-Fi.”
• Epsilon: A Visible Light Based Positioning System (2014, background): “Most existing localization work leverages signals such as WiFi [3, 6, 20, 21, 23], FM [5], magnetism [7].”
• A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned (2015, background): “Infrastructure-free approaches have focused on leveraging already existing WiFi [5, 15, 49, 40, 41, 45, 6, 22, 47, 50], FM and TV [8, 9, 34, 28, 29, 27, 12, 33, 19, 48], GSM [31, 44], geo-magnetic [10], and sound signals [43] to enable indoor localization through detailed fingerprinting.”