LANDMARC: Indoor Location Sensing Using Active RFID

By Lionel M. Ni, Yunhao Liu, Yiu Cho Lau, Abhishek P. Patil. 2003

3,020 citations recorded by the source corpus.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Media Technology

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Citations in the corpus

Non-GPS Positioning Systems (2017, methodology): “Additionally, LEMON [40] localizes moving target or person using the fingerprinting approach and does not restrict the number of sensor nodes as compared to older methods such as RADAR [11] and LANDMARC [94].”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “Radio Frequency Identification (RFID) depends on response between RFID readers and RFID tags for tracking [8], [126], [193].”
Issues in data fusion for healthcare monitoring (2008, neutral): “Moreover, active RFID based localization schemes can be employed in the indoor health care system such as LANDMARC [33] and its variation [49].”
RFID tags: Positioning principles and localization techniques (2008, methodology): “Landmarc [9] (2003) kNN Yes Active 2-D VIRE [10] (2007) kNN Yes Active 2-D Simplex [11] (2007) kNN optimization Yes Active 3-D Kalman filtering [12] (2007) RSS mean squares and Kalman filetering Yes Active 2-D Scout [13] (2006) RSS Bayesian approach Yes Active 2-D”
Transferring Localization Models over Time (2008, neutral): “90% 0.4 0.5 0.6 0.7 0.8 0.9 1 07:10pm Ratio of locations used for reference points Culmulative probability TrHMM LeManCoR RADAR LANDMARC (b) Impact of reference points for 07:10pm 0 5 10 15 20 25 30 0.65 0.7 0.75 0.8 0.85 0.9 04:21pm Number of unlabled traces Culmulative probability TrHMM UnHMM LeMa”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “LANDMARC [Ni et al. 2004] is the first attempt to apply active RFID tags for localization.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “Many well known multi-hop localization algorithms, such as APS by Niculescu and Nath (2001), estimate the shortest-path distances (in terms of actual distances or number of hops) between sensor nodes and derive an overall Euclidean distance from this to estimate position.”
Indoor localization without the pain (2010, methodology): “Recently, RFID based systems such as LANDMARC [19] also have been proposed.”
FILA: Fine-grained indoor localization (2012, methodology): “RFID has also been used for indoor localization [19]”
Zee: zero-effort crowdsourcing for indoor localization (2012, background): “There have also been localization systems based on a deployment of RF transmitters and sniffers [15] or RFID [18].”
CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, background): “different forms of fingerprinting have been explored, including WiFi [6], FM radio [24], RF identification [25], acoustic [26],”
Indoor Human Navigation Systems: A Survey (2013, background): “While passive tags are much less expensive, they have much shorter range and can store much less data [49], which increases their installation cost.”
WILL: Wireless indoor localization without site survey (2012, methodology): “Some systems, such as LANDMARC [11] , utilize RFID for indoor localization.”
Location, Localization, and Localizability (2010, background): “The neighborhood information can be more useful when the density of reference nodes is sufficiently high so that there are often multiple reference nodes within the range of an unknown node([42]).”