Abstract
Radio Frequency Identification (RFID) technology is widely used in object tracking and tracing, especially in real-time locating system (RTLS). Due to the external and internal influence of RFID systems, a lot of redundant and uncertain location streams could be generated in RFID-based RTLS applications, which could seriously affect the accuracy of estimation for RFID mobile object position and cause great difficulties in RFID-based RTLS applications. In this paper, we systematically analyzed the characteristics of RFID location streams. We then derived the optimal weight for the attributes of RFID location streams by applying information entropy based methods and used probability matrix to optimize weight attributes in location streams. We also proposed an optimal estimation particle filter algorithm (OEPF) based on traditional particle filter, which greatly reduced the data redundancy and realized online measurement for the uncertainty of RFID location streams. Finally, the experimental results showed that, compared to the existing algorithms, our algorithm effectively improved the accuracy of location estimation in ensuring the premise of real-time.
Highlights
RFID (Radio Frequency Identification) technology now is widely used in real-time location-based applications, due to the impact of the sensor itself and the surrounding environment, resulting in a large number of uncertain RFID location streams in these applications [1,2,3]
The RFID positioning applications could generate a lot of redundant and uncertain location streams, which would greatly affect the accuracy of RFID positioning
We improved and optimized the particle filter algorithm and proposed a particle filter algorithm based on attributes optimization estimation
Summary
RFID (Radio Frequency Identification) technology now is widely used in real-time location-based applications, due to the impact of the sensor itself and the surrounding environment, resulting in a large number of uncertain RFID location streams in these applications [1,2,3]. Zheng et al [7] proposed a novel statistical method which could dynamically adapt the size of the particle filter sample set, using evolutionary theory to solve particle degradation problem and improved Particle Swarm Optimization (PSO) to optimize resampling performance. They solved the conflicts between effectiveness and diversity in resampling which is caused by relying solely on the KLdivergence [8] to determine the number of particles. Capture the current state from the uncertain RFID data streams, while reducing the required number of resampling particles, which is very suitable for online processing of realtime RFID data streams
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