Nothing Special   »   [go: up one dir, main page]

Pipelidis et al., 2019 - Google Patents

Cross-device radio map generation via crowdsourcing

Pipelidis et al., 2019

Document ID
11054259577488165155
Author
Pipelidis G
Tsiamitros N
Ustaoglu E
Kienzler R
Nurmi P
Flores H
Prehofer C
Publication year
Publication venue
2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

External Links

Snippet

Crowdsourcing is a powerful technique for bootstrapping sensing systems that are based on wireless signals. For example, wireless sensing systems can ask users to contribute training data and localization systems (such as WiFi fingerprinting) can take advantage of wireless …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W4/00Mobile application services or facilities specially adapted for wireless communication networks
    • H04W4/02Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
    • H04W4/025Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS using location based information parameters
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W4/00Mobile application services or facilities specially adapted for wireless communication networks
    • H04W4/02Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
    • H04W4/023Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves by comparing measured values with pre-stored measured or simulated values
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organizing networks, e.g. ad-hoc networks or sensor networks
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205Details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores

Similar Documents

Publication Publication Date Title
Chriki et al. SVM-based indoor localization in wireless sensor networks
Ibrahim et al. CNN based indoor localization using RSS time-series
Zheng et al. Exploiting fingerprint correlation for fingerprint-based indoor localization: A deep learning-based approach
Shu et al. Gradient-based fingerprinting for indoor localization and tracking
Cui et al. Received signal strength based indoor positioning using a random vector functional link network
US9807549B2 (en) Systems and methods for adaptive multi-feature semantic location sensing
Tian et al. Fingerprint indoor positioning algorithm based on affinity propagation clustering
Zhao et al. GraphIPS: Calibration-free and map-free indoor positioning using smartphone crowdsourced data
Lu et al. Robust occupancy inference with commodity WiFi
Zhu et al. Accurate WiFi-based indoor localization by using fuzzy classifier and mlps ensemble in complex environment
Rizk et al. A ubiquitous and accurate floor estimation system using deep representational learning
Pei et al. FCN-Attention: A deep learning UWB NLOS/LOS classification algorithm using fully convolution neural network with self-attention mechanism
Ai et al. DRVAT: Exploring RSSI series representation and attention model for indoor positioning
Pipelidis et al. Cross-device radio map generation via crowdsourcing
Abuhoureyah et al. Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information
Jain et al. Performance analysis of received signal strength fingerprinting based distributed location estimation system for indoor wlan
Wietrzykowski et al. Adopting the FAB-MAP algorithm for indoor localization with WiFi fingerprints
De Vita et al. A deep learning approach for indoor user localization in smart environments
Zheng et al. RSS-based indoor passive localization using clustering and filtering in a LTE network
Carrera et al. Discriminative learning-based smartphone indoor localization
Abdullah et al. K-means-Jensen-Shannon divergence for a WLAN indoor positioning system
Xing et al. Integrated segmentation and subspace clustering for RSS-based localization under blind calibration
Sanam et al. CoMuTe: A convolutional neural network based device free multiple target localization using CSI
Jiang et al. WiDE: WiFi distance based group profiling via machine learning
Tran et al. Fingerprint-based location tracking with hodrick-prescott filtering