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

By Antonio Ramón Jiménez Ruiz, Fernando Seco Granja, José Carlos Prieto, Jorge I. Guevara Rosas. IEEE Transactions on Instrumentation and Measurement, 2012

445 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/99848872956e48a17bf023e47e9cf6427090fedd

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In Biomedical Engineering

1. Harald Haas
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4. Zhaochu Yang
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Citations in the corpus

• Learning Switching Models for Abnormality Detection for Autonomous Driving (2018, background): “State estimation is important for motion prediction [1], [2], tracking [3–5], identification”
• A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, background): “DR systems are most often constructed using Inertial Measurement Unit (IMU) devices that contain accelerometers used for step detection and step length estimation and magnetic compasses or low-cost gyros for heading determination [45].”
• A Theoretical Foundation of Network Localization and Navigation (2018, unknown): “[78] A.”
• A Survey of Positioning Systems Using Visible LED Lights (2018, background): “, WiFi [4], [5], Bluetooth [6], [7], radio frequency identification (RFID) [8], [9], and ZigBee [10]) have been proposed to fill the gap of GPS signals to improve the performance of indoor positioning.”