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An RFID Tag Movement Trajectory Tracking Method Based on Multiple RF Characteristics for Electronic Vehicle
Ruoyu Pan1, Zhao Han1, Tuo Liu1
1School of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
This study introduces a new method for tracking vehicle trajectories using radio frequency identification (RFID) tags. The system achieves high-precision positioning, crucial for intelligent transportation systems (ITS).
Area of Science:
- Engineering
- Computer Science
- Transportation Science
Background:
- Intelligent transportation systems (ITS) require accurate vehicle identification and monitoring, especially under high-speed conditions.
- Radio Frequency Identification (RFID) and Electronic Vehicle Identification (EVI) offer continuous, high-accuracy data for traffic analysis and urban planning.
Purpose of the Study:
- To develop an RFID tag motion trajectory tracking method using multiple radio frequency (RF) features for ITS.
- To enhance vehicle positioning and trajectory analysis at critical traffic checkpoints.
Main Methods:
- Proposed an RFID tag motion trajectory tracking method utilizing multiple RF features.
- Analyzed the relationship between Received Signal Strength Indicator (RSSI), phase differences, and driving distances.
- Employed an information weight method to determine feature weights at varying distances.
- Calculated the common area center point for vehicle localization under multi-antenna conditions.
Main Results:
- Achieved an average positioning error of less than 17 cm for moving RFID tags.
- Demonstrated the effectiveness of using dual-frequency signal phase differences and RSSI for accurate tracking.
- Validated the method's capability for real-time, high-precision vehicle positioning.
Conclusions:
- The developed method provides a robust solution for real-time vehicle positioning and trajectory tracking in ITS.
- Applicable to scenarios like parking guidance, autonomous vehicle route monitoring, and lane change detection.
- Significantly improves the accuracy and reliability of vehicle tracking in dynamic traffic environments.
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