Abstract
Under-determined blind separation becomes poorer with increasing number of tags, to the point where it cannot separate source tag signals, reducing overall system performance. This paper proposes a parallelizable identification anti-collision algorithm based on non-negative matrix factorization and adaptive ID sequence grouping of binary tree slots. The number of tags in each group can be controlled within the optimum range by selecting a reasonable number to retained source signal separation in the RFID system, which will greatly improve system performance. With the Matlab software numerical calculation and simulation, the results show that tag identification rate improves from 152.8% to 359.2% compared with the blind separation and dynamic bit-slot group algorithm using the same multi-antenna technology for 4–16 antennas, while increasing tag identification speed from 60% to 78.4%. Thus, the proposed algorithm provides high efficiency and low cost and will have very good application for fast identification of large numbers of tags.
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Acknowledgments
This work is jointly supported by the National Natural Science Foundation of China (Nos. 61763017, 51665019), Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202053, 20161BAB206145).
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Zhang, X., Wang, Q., Jin, Y. (2019). Under-Determined Blind Source Separation Anti-collision Algorithm for RFID Based on Adaptive Tree Grouping. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_23
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DOI: https://doi.org/10.1007/978-3-030-24271-8_23
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