Indoor localization without infrastructure using the acoustic background spectrum
By Stephen P. Tarzia, P. Dinda, R. Dick, G. Memik. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2011
332 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/12060bd60d55b33fe64b7f6cfb058b247268d1cf
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
- Shiwen Mao
- Xuyu Wang
- Lingjun Gao
- Santosh Pandey
- Bodhi Priyantha
- Souvik Sen
- Dimitrios Lymberopoulos
- Feng Zhao
In Signal Processing
- Shiwen Mao
- Xuyu Wang
- Lingjun Gao
- Moustafa Youssef
- Santosh Pandey
- Romit Roy Choudhury
- Souvik Sen
- Feng Zhao
Citations in the corpus
- No need to war-drive: unsupervised indoor localization (2012, background): “While ambient signatures and activity recognition have been utilized in the past [14, 28], its applicability towards deadreckoning is, to the best of our knowledge, novel.”
- A reliable and accurate indoor localization method using phone inertial sensors (2012, background): “Because of the lack of reliable GPS signals inside a building, researchers have explored the use of WiFi beacons [2, 27], magnetometer [6], vision [10], or ultrasound [24], to name a few, for coarse level indoor positioning.”
- CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, background): “different forms of fingerprinting have been explored, including 820 WiFi [6], FM radio [24], RF identification [25], acoustic [26], 821”
- 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.”
- WILL: Wireless indoor localization without site survey (2012, unknown): “And [13], [14], [15] utilizes FM Radio, acoustic background spectrum (ABS) and geo-magnetism respectively as fingerprints for indoor location estimation.”
- FM-based indoor localization (2012, background): “With data from different time and day, however, the localization accuracy drops to 70%, as pointed out in [22].”