Retracted: A Novel Radio Frequency Identification Collision Resolution Method Based on Statistical Learning - Zhong Dongbo (IEEE Access)
In the application scenarios of radio frequency identification technology, there are many situations where a large number of labels respond to the reader at the same time, resulting in the labels not being able to be identified for a long time. In order to address the label collision problem of radio frequency identification, this paper studies the impact of statistical learning method on the resolution and decoding of collision labels, and proposes a novel clustering method using maximum posteriori probability estimation based on Monte-Carlo. Unlike traditional algorithms, the proposed method does not require prior knowledge of the number of clusters and does not need to constantly iterate. In addition, this method has low complexity and ensures both accuracy and robustness while quickly finding the cluster centroids. Finally, the resolution performance of the proposed method is evaluated based on the simulation experiment and the field experiment, and the resolved signals are decoded using matched filter and phase jump. Overall, the effectiveness of the our method is demonstrated through comparisons with different performance metrics of different benchmark methods, including bit error rate, resolution efficiency, throughput, error, and time complexity.