Locating in fingerprint space: wireless indoor localization with little human intervention

By Zheng Yang, Chenshu Wu, Yunhao Liu. ACM/IEEE International Conference on Mobile Computing and Networking, 2012

785 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/8254757a71a2c3611fd4f3b70d27dad869456784

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

In Ocean Engineering

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

In Signal Processing

1. Shiwen Mao
2. Xuyu Wang
3. Lingjun Gao
4. Jie Xiong
5. Kyle Jamieson
6. Kun Qian
7. S. Chan
8. Suining He

Citations in the corpus

• ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, background): “Zee [37] and LiFS [63] leverage crowdsourcing and inertial dead reckoning to eliminate the calibration that RSSI fingerprinting requires.”
• Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, background): “This is a non-trivial problem and has been actively studied recently [19], [20].”
• Phaser: enabling phased array signal processing on commodity WiFi access points (2014, methodology): “To build the radio map without human involvement, follow-on work uses crowdsourcing, step counting, smartphone inertial sensors [18, 26, 33], and GPS [6].”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, methodology): “Similarly, LiFS [32] also uses user trajectories to obtain fingerprint values and then builds the mapping between the fingerprints and the floor plan.”
• Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “Some research shows that due to uncertainty in signal collection localization accuracy under survey reduction is relatively lower than that based on traditional fingerprinting [138].”
• Avoiding multipath to revive inbuilding WiFi localization (2013, background): “Realizing this difficulty, there has been efforts to reduce the overhead of wardriving by using crowdsourcing techniques [10, 11].”
• Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications (2017, background): “Here, we mention the works of [82] exploiting”
• SpotFi: Decimeter Level Localization Using WiFi (2015, background): “…idea of these approaches is that one would collect fingerprints such as the vector of RSSIs from a specific location to all the APs in range, and if in the future a target were at the same location, it would exhibit the same fingerprint, enabling us to localize [14, 15, 16, 17, 18, 19, 20, 21, 22].”
• Fusing Similarity-Based Sequence and Dead Reckoning for Indoor Positioning Without Training (2017, background): “Therefore, some researchers proposed different techniques to reduce this phase [11]–[13].”
• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “Various signal features, from RSSI [27] to CSI [13], have been exploited as location fingerprints.”
• Recent Advances in Wireless Indoor Localization Techniques and System (2013, background): “Yang et al. [46] proposed a wireless indoor localization approach called Locating in fingerprint space (LiFS).”
• Accurate indoor localization with zero start-up cost (2014, background): “The second mandates signal fingerprinting [3, 27, 39] either in a training phase or via crowdsourcing [26, 38, 18].”
• CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “LiFS [27] utilizes users trajectories to get fingerprint values and then creates the mapping between the fingerprints and the floor plan.”
• Deep Neural Networks for wireless localization in indoor and outdoor environments (2016, positive): “Some other work [33] proposed to improve the accuracy of fingerprint method by introducing user intervention.”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “LiFS [Yang et al. 2012] further reduces the manual cost of site survey by exploring the connection of user motions and previous RSS fingerprints.”
• Walkie-Markie: Indoor Pathway Mapping Made Easy (2013, methodology): “Several recent efforts aimed at alleviating the pain of radio map construction require knowledge of the real floor plans [26] [38].”