[go: up one dir, main page]

Villacrés et al., 2019 - Google Patents

A particle filter-based reinforcement learning approach for reliable wireless indoor positioning

Villacrés et al., 2019

Document ID
14095809407851532868
Author
Villacrés J
Zhao Z
Braun T
Li Z
Publication year
Publication venue
IEEE journal on selected areas in communications

External Links

Snippet

Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users' positions. In this work, we propose a particle filter-based reinforcement learning (PFRL) …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • 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
    • G01S5/0284Relative positioning
    • G01S5/0289Relative positioning of multiple transceivers, e.g. in ad hoc 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
    • G01S5/0257Hybrid positioning solutions
    • 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
    • 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
    • G01S19/00Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42Determining position
    • 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
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant
    • G01S13/72Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726Multiple target tracking
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/20Instruments for performing navigational calculations
    • G01C21/206Instruments for performing navigational calculations specially adapted for indoor navigation
    • 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
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/87Combinations of radar systems, e.g. primary radar and secondary radar
    • G01S13/878Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector
    • 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
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/10Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration
    • G01C21/12Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation

Similar Documents

Publication Publication Date Title
Villacrés et al. A particle filter-based reinforcement learning approach for reliable wireless indoor positioning
Liu et al. Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information
Wu et al. Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization
Pei et al. A survey of crowd sensing opportunistic signals for indoor localization
Wei et al. RSSI-based location fingerprint method for RFID indoor positioning: A review
Chen et al. Drunkwalk: Collaborative and adaptive planning for navigation of micro-aerial sensor swarms
Elbes et al. An indoor localization approach based on deep learning for indoor location-based services
Zhao et al. A real-time robust indoor tracking system in smartphones
Scholz et al. Resource-aware on-line RFID localization using proximity data
Shit et al. AI-enabled fingerprinting and crowdsource-based vehicle localization for resilient and safe transportation systems
Subedi et al. Improved smartphone-based indoor localization system using lightweight fingerprinting and inertial sensors
Shokry et al. DynamicSLAM: Leveraging human anchors for ubiquitous low-overhead indoor localization
Yadav et al. Unveiling the cutting edge: a comprehensive survey of localization techniques in WSN, leveraging optimization and machine learning approaches
Zhou et al. An indoor positioning algorithm based on particle filter and neighbor-guided particle optimization for wireless sensor networks
Latif et al. Dgorl: Distributed graph optimization based relative localization of multi-robot systems
Vahidnia et al. A hierarchical signal-space partitioning technique for indoor positioning with WLAN to support location-awareness in mobile map services
Huang et al. VariFi: Variational inference for indoor pedestrian localization and tracking using IMU and WiFi RSS
Henriques Abreu et al. Using Kalman filters to reduce noise from RFID location system
Varma et al. Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence
Wang et al. Leto: crowdsourced radio map construction with learned topology and a few landmarks
Tinh et al. A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data
Fahama et al. Indoor Localization using RSSI based Supervised Machine Learning Approaches
Carrera et al. Room recognition using discriminative ensemble learning with hidden markov models for smartphones
Carrera et al. Discriminative learning-based smartphone indoor localization
Chan et al. Fusion-based smartphone positioning using unsupervised calibration of crowdsourced Wi-fi FTM