Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

• Published in: IEEE transactions on instrumentation and measurement Vol. 61; no. 1; pp. 178 - 189
• Main Authors: Jimenez Ruiz, Antonio Ramón, Seco Granja, Fernando, Prieto Honorato, José Carlos, Guevara Rosas, Jorge I.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Buildings; Dead reckoning; Drift; Errors; Estimation; Foot; Indoor; Inertial; inertial navigation; Joining; Kalman filters; Mathematical models; Methodology; Position measurement; Radio frequency identification; RFID tags
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2011.2159317

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.