Jove
Visualize
Contact Us

Related Concept Videos

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

296
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
296
Errors in Global Positioning System01:26

Errors in Global Positioning System

317
Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
317
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

330
GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
330

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

BLE-Based Custom Devices for Indoor Positioning in Ambient Assisted Living Systems: Design and Prototyping.

Sensors (Basel, Switzerland)·2025
Same author

RegulEm, a smartphone app based on the unified protocol for the transdiagnostic treatment of emotional disorders: description of its application in blended format in a clinical case.

Digital health·2025
Same author

Signal Processing and Machine Learning for Smart Sensing Applications.

Sensors (Basel, Switzerland)·2023
Same author

Advances in Indoor Positioning and Indoor Navigation.

Sensors (Basel, Switzerland)·2022
Same author

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review.

Sensors (Basel, Switzerland)·2022
Same author

Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments.

Scientific data·2022
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 10, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.2K

From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning

Sara Martín-Frechina1, Esther Dura1, Ignacio Miralles1

  • 1Department of Computer Science, ETSE, University of Valencia, Avda. de la Universidad, S/N, 46100 Burjassot, Valencia, Spain.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

This review explores Machine Learning (ML) for Indoor Positioning Systems (IPS) using Wi-Fi and Bluetooth. It highlights Deep Learning (DL) advancements and identifies challenges like environmental variability for future research.

Keywords:
Angle of Arrival (AoA)Bluetooth Low Energy (BLE)Channel State Information (CSI)Deep Learning (DL)IEEE 802.11 Wireless LAN (Wi-Fi)Indoor Positioning System (IPS)Machine Learning (ML)Received Signal Strength Indicator (RSSI)Round Trip Time (RTT)

More Related Videos

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Related Experiment Videos

Last Updated: Jan 10, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

7.2K
Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Indoor Positioning Systems (IPS) are crucial for smart environments, evolving from basic signal strength to advanced techniques.
  • Methods like Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA) are increasingly integrated with Machine Learning (ML).

Purpose of the Study:

  • To systematically review Machine Learning-based Indoor Positioning Systems (IPS) utilizing IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE).
  • To analyze measurement techniques, ML models, and the rise of Deep Learning (DL) in IPS literature from 2020-2024.

Main Methods:

  • Systematic literature review adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology.
  • Analysis of studies published between 2020 and 2024 focusing on ML applications in Wi-Fi and BLE-based IPS.

Main Results:

  • Identified a growing trend in the application of Deep Learning (DL) approaches for indoor positioning.
  • Examined various measurement collection techniques and ML models employed in recent IPS research.
  • Highlighted persistent implementation challenges including environmental variability, device heterogeneity, and calibration requirements.

Conclusions:

  • Future research should focus on expanding ML to RTT and AoA, developing hybrid multimetric systems, and creating efficient, adaptive DL models.
  • Advances in wireless standards and emerging technologies are key to enhancing the accuracy and scalability of next-generation IPS.