Mobile target indoor tracking based on Multi-Direction Weight Position Kalman Filter

Radio Frequency Identification (RFID)-based fingerprint indoor positioning and tracking technology is one of the key technologies in the study of wireless sensor network, and has been widely used in noisy environment. However, due to the time and space fluctuation in Received Signal Strength Indicat...

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Bibliographic Details
Published inComputer networks (Amsterdam, Netherlands : 1999) Vol. 141; pp. 115 - 127
Main Authors Zhu, Dali, Zhao, Bobai, Wang, Siye
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 04.08.2018
Elsevier Sequoia S.A
Subjects
Accuracy
Algorithms
Data acquisition
Fingerprint localization
Fingerprinting
Fingerprints
Indoor localization
Kalman filters
Noise reduction
Position Kalman filter
Radio frequency identification
Remote sensors
RFID
Signal strength
Tracking
Variation
Velocity
Weight
Wireless networks
Wireless sensor networks
99-00
RFID
Fingerprint localization
00-01
Indoor localization
Position Kalman filter
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Summary:Radio Frequency Identification (RFID)-based fingerprint indoor positioning and tracking technology is one of the key technologies in the study of wireless sensor network, and has been widely used in noisy environment. However, due to the time and space fluctuation in Received Signal Strength Indicator (RSSI) of RFID, indoor positioning accuracy is not satisfactory. In this work, we present a Multi-Direction Weight Position Kalman Filter (MDWPKF) according to the spacial feature of RSSI. This algorithm combines the Multi-Direction data collection method, with Standard Kalman Filter and fingerprint matching algorithm to achieve the signal fluctuation reduction, noise removal and 2D fingerprint mapping. At the same time, the Improved Position Kalman Filter (IPKF) in our proposed MDWPKF takes the advantages of Gaussian weight computation and velocity estimator to refine the position and velocity estimates. Compared with traditional PKF, the MDWPKF improves the positioning accuracy by 17.7%, and the velocity accuracy by 10.2%. Compared with Fingerprint Kalman Filter (FKF), the MDWPKF can be used for the tracking of both moving target (including position and velocity estimates) and stationary object.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2018.05.021