{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:10:31Z","timestamp":1771701031523,"version":"3.50.1"},"reference-count":11,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2011,10,28]],"date-time":"2011-10-28T00:00:00Z","timestamp":1319760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.<\/jats:p>","DOI":"10.3390\/s111110266","type":"journal-article","created":{"date-parts":[[2011,10,28]],"date-time":"2011-10-28T11:04:29Z","timestamp":1319799869000},"page":"10266-10282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems"],"prefix":"10.3390","volume":"11","author":[{"given":"Ki Hwan","family":"Eom","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]},{"given":"Seung Joon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]},{"given":"Yeo Sun","family":"Kyung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]},{"given":"Chang Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]},{"given":"Min Chul","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]},{"given":"Kyung Kwon","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2011,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Turcu, C. (2009). Development and Implementation of RFID Technology, InTech.","DOI":"10.5772\/100"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ho, L., Moh, M., Walker, Z., Hamada, T., and Su, C. (2005, January 22). A Prototype on RFID and Sensor Networks for Elder Healthcare: Progress Report. Philadelphia, PA, USA.","DOI":"10.1145\/1080148.1080164"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.aeue.2008.06.003","article-title":"Fault Detection in Sensor Information Fusion Kalman Filter","volume":"63","author":"Okatan","year":"2009","journal-title":"Int. J. Electron. Commun"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1109\/78.91144","article-title":"Filtering of Colored Noise for Speech Enhancement and Coding","volume":"39","author":"Gibson","year":"1991","journal-title":"IEEE Trans. Sign. Process"},{"key":"ref_5","unstructured":"Hagan, M., Demuth, H., and Beale, M. (1996). Neural Network Design, PWS Publishing."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.engappai.2004.10.003","article-title":"Adaptive Neural Model-Based Fault Tolerant Control for Multi-Variable Processes","volume":"18","author":"Yu","year":"2005","journal-title":"Eng. Appl. Artif. Intell"},{"key":"ref_7","unstructured":"Wang, J., Ding, W., and Wang, J. (2007, January 25\u201328). Improving Adaptive Kalman Filter in GPS\/SDINS Integration with Neural Network. Fort Worth, TX, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"Trans. ASME J. Basic Eng"},{"key":"ref_9","unstructured":"Haykin, S. (1999). Neural Networks (A Comprehensive Foundation), Prentice-Hall International."},{"key":"ref_10","unstructured":"Welch, G., and Bishop, G. (1995). An Introduction to the Kalman Filter, Department of Computer Science, University of North Carolina. Technical Report 95-041;."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Grewal, M.S., and Andrew, A.P. (2001). Kalman Filtering: Theory and Practice, John Wiley & Sons.","DOI":"10.1002\/0471266388"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/11\/10266\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:57:51Z","timestamp":1760219871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/11\/10266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,10,28]]},"references-count":11,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2011,11]]}},"alternative-id":["s111110266"],"URL":"https:\/\/doi.org\/10.3390\/s111110266","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,10,28]]}}}