Jove
Visualize
Contact Us
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: May 24, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.

Ki Hwan Eom1, Seung Joon Lee, Yeo Sun Kyung

  • 1Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea. kihwanum@dongguk.edu

Sensors (Basel, Switzerland)
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Revisiting Postprocedural Anticoagulation After Primary PCI: Lessons From the RIGHT Trial.

JACC. Asia·2026
Same author

Identifying Rate-Limiting Steps in the Antimicrobial Workflow for Community-Onset Sepsis.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

2025 Korean Guidelines for Cardiopulmonary Resuscitation: Part 6. Post-cardiac arrest care.

Clinical and experimental emergency medicine·2026
Same author

Clinical Outcomes and Use of Implantable Cardioverter-Defibrillator in Ischemic Heart Failure Patients with Reduced Ejection Fraction: A Retrospective Observational Study.

Chonnam medical journal·2026
Same author

Combination of Fimasartan and Statins in Patients With Hypertension and Dyslipidemia.

Journal of lipid and atherosclerosis·2026
Same author

Nanoparticle-enriched mass spectrometry enables comprehensive plasma proteomics and reveals novel candidates for mild cognitive impairment.

Scientific reports·2026

This study introduces an improved Kalman filter for smart radio frequency identification (RFID) systems. The method enhances sensor data accuracy by reducing noise, significantly lowering mean squared error.

Area of Science:

  • Sensor Technology
  • Signal Processing
  • Artificial Intelligence

Background:

  • Smart radio frequency identification (RFID) tags offer environmental monitoring capabilities.
  • Accurate sensor measurements are critical for smart RFID system performance.
  • Noisy signals are a common challenge in multi-sensing environments due to changing surroundings.

Purpose of the Study:

  • To propose an improved Kalman filter method for noise reduction in smart RFID systems.
  • To enhance the accuracy of sensor data obtained from smart RFID tags.
  • To optimize Kalman filter performance by focusing on measurement noise covariance.

Main Methods:

  • Developed an improved Kalman filter algorithm.
  • Focused on adjusting the measurement noise covariance (R variable).
Keywords:
Kalman filtermeasurement noise reductionmulti-sensing environmentneural networksmart RFID tags

More Related Videos

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Related Experiment Videos

Last Updated: May 24, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

  • Utilized a neural network for system architecture adjustment.

    • Main Results:

    • The proposed improved Kalman filter demonstrated significant noise reduction capabilities.
    • Achieved 40.1% less mean squared error (MSE) for temperature sensors.
    • Achieved 60.4% less MSE for humidity sensors and 87.5% less MSE for oxygen sensors compared to conventional methods.
    • Experimental validation confirmed the simulation results.

    Conclusions:

    • The improved Kalman filter effectively reduces noise and enhances data accuracy in smart RFID systems.
    • The method offers a significant improvement over conventional Kalman filters for various sensor types.
    • This approach is crucial for reliable environmental monitoring using smart RFID technology.