RFID标签防碰撞中有界成分分析算法的优化
作者:
作者单位:

1.四川轻化工大学自动化与信息工程学院 宜宾 644000; 2.四川轻化工大学人工智能四川省重点实验室 宜宾 644000

中图分类号:

TN911.23

基金项目:

国家自然科学基金(61801319)、四川省科技厅项目(2020JDJQ0061,2021YFG0099)、四川轻化工大学研究生创新基金(Y2023273)、四川轻化工大学科研创新团队项目(SUSE652A011)资助


Optimization of bounded component analysis algorithm in RFID tag anti-collision
Author:
Affiliation:

1.School of Automation and Information Engineering, Sichuan University of Science and Engineering,Yibin 644000, China; 2.Artificial Intelligence key Laboratory of Sichuan Province, Sichuan University of Science and Engineering,Yibin 644000, China

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    摘要:

    为了更好地解决RFID系统的欠定防碰撞问题,基于盲源分离的方法从初始化分离矩阵的角度对分离算法进行了优化。由于混合矩阵确定了源信号与观测信号之间的线性映射关系,直接影响了分离算法的收敛性和分离结果的质量,因此初始混合矩阵的选择对算法的性能和有效性至关重要。利用连续非负投影算法计算出初始的混合矩阵,摒弃传统的随机初始化,避免了算法陷入局部最优解。由于RFID的标签信号都是有界的,因此在下一步使用有界成分分析算法从混合信号中将标签信号分离出来。仿真结果表明,此算法相较于传统有界成分分析算法的分离相似度在低信噪比下提升了3.05%,比常用的非负矩阵分解算法提高了6.64%的分离准确率。其较低的误码率也表明系统在数据传输或接收过程中能够有效地处理干扰和噪声,从而减少数据错误的发生。

    Abstract:

    In order to better solve the underdetermined anti-collision problem of RFID system, the separation algorithm is optimized from the perspective of initializing the separation matrix based on the blind source separation method. Since the mixing matrix determines the linear mapping relationship between the source signal and the observed signal, it directly affects the convergence of the separation algorithm and the quality of the separation results. Therefore, the selection of the initial mixing matrix is crucial to the performance and effectiveness of the algorithm. The initial mixing matrix is calculated using the successive nonnegative projection algorithm, which abandons the traditional random initialization and avoids the algorithm from falling into the local optimal solution. Since the tag signals of RFID are bounded, the bounded component analysis algorithm is used in the next step to separate the tag signal from the mixed signal. The simulation results show that the separation similarity of this algorithm is improved by 3.05% compared with the traditional bounded component analysis algorithm at low signal-to-noise ratio, and the separation accuracy is improved by 6.64% compared with the commonly used non-negative matrix factorization algorithm. Its low bit error rate also shows that the system can effectively handle interference and noise during data transmission or reception, thereby reducing the occurrence of data errors.

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王玲,骆忠强. RFID标签防碰撞中有界成分分析算法的优化[J].电子测量技术,2024,47(24):49-56

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  • 在线发布日期: 2025-01-24
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