Survey of Wireless Indoor Positioning Techniques and Systems

By Hui Liu, Houshang Darabi, Pat P. Banerjee, J. Liu. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2007

4,444 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d22fc46a607dbc16dfc9a32084fdd8b2558da17c

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Citations in the corpus

An in-Depth Survey of Visible Light Communication Based Positioning Systems (2016, methodology): “In [19], wireless indoor positioning techniques were classified into three groups: triangulation, scene analysis (also known as fingerprinting), and proximity.”
Position Accuracy of Time-of-Arrival Based Ranging Using Visible Light With Application in Indoor Localization Systems (2013, background): “Despite extensive research on RF-based solutions, the major challenge of strong multipath interference means that there is no accurate, low cost, low power system available [1], [2].”
Indoor Positioning Technologies (2012, background): “An extensive survey of wireless indoor positioning techniques and solutions has been carried out by Liu et al. (2007).”
A Survey of Indoor Inertial Positioning Systems for Pedestrians (2013, background): “At a minimum, these algorithms must permit for accurate step counting, although many systems also require accurate step segmentation.”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “In wireless communication networks, radio localization [218]”
RSSI-Based Indoor Localization With the Internet of Things (2018, methodology): “AoA systems use an array of antennae to determine the angle, from which the signal propagated [8], [19], [20].”
A survey of active and passive indoor localisation systems (2012, methodology): “These technologies may be classified according to the location positioning algorithm, the physical layer or location sensor infrastructure [8].”
A survey on indoor positioning systems (2014, methodology): “In [1], the considered indoor localization approach is based on the Wireless Sensor Network (WSN) technology whereas in [2] the Radio Frequency Identification (RFID) technology is explored.”
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, background): “K-nearest-neighbor [4], neural networks [5], and support vector machines [5], have been adopted to extract and store the main features (or learning parameters) of survey data instead, and can thus achieve better localization performance.”
D2D for Intelligent Transportation Systems: A Feasibility Study (2015, background): “Actually, due to the high complexity of channel estimation in obtaining the full CSI [45], [46], we believe that the partial CSI instead of the full CSI will be more suitable for vehicular networks from the perspective of both practicality and effectiveness.”
TDOA-based optical wireless indoor localization using LED ceiling lamps (2011, methodology): “To use the triangulation technique, it is required to measure the angle or distance between a reference point and a mobile terminal, and there are several methods to measure for this purpose such as, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signa”
Non-GPS Positioning Systems (2017, methodology): “It works well under LOS conditions, otherwise the performance degrades severely [71].”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “a set of closest RPs are selected, the method is known as K-Nearest Neighborhood (KNN) [14], [69], [75], [78].”
A Review of Indoor Localization Techniques and Wireless Technologies (2021, methodology): “In the case of using RSS fingerprinting, cell stations are regarded as the APs; the accuracy was found to be in the range of 2.5–5.4 m [74].”
DeepFi: Deep learning for indoor fingerprinting using channel state information (2015, methodology): “K-nearestneighbor uses the weighted average of K nearest locations to determine an unknown location with the inverse of the Euclidian distance between the observed RSS measurement and its K nearest training samples as weights [1].”