Twice Labels Number Estimation Algorithm Based on Gaussian Fitting and Chebyshev Inequality

Junrong YAN; Renjie YE; Hua ZHONG; Xianyang JIANG

doi:10.11999/JEIT200209

Volume 43 Issue 7

Jul. 2021

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Junrong YAN, Renjie YE, Hua ZHONG, Xianyang JIANG. Twice Labels Number Estimation Algorithm Based on Gaussian Fitting and Chebyshev Inequality[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1893-1899. doi: 10.11999/JEIT200209

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Junrong YAN, Renjie YE, Hua ZHONG, Xianyang JIANG. Twice Labels Number Estimation Algorithm Based on Gaussian Fitting and Chebyshev Inequality[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1893-1899. doi: 10.11999/JEIT200209

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Twice Labels Number Estimation Algorithm Based on Gaussian Fitting and Chebyshev Inequality

doi: 10.11999/JEIT200209 cstr: 32379.14.JEIT200209

School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: Hangzhou Dianzi University Research Innovation Fund 2019, Zhejiang Provincial Public Technology Application Research Program(2017C31055)

Received Date: 2020-03-25
Rev Recd Date: 2020-12-06

Available Online: 2020-12-16

Publish Date: 2021-07-10

Abstract

Abstract

In order to solve the problems of large estimation error, prolonged identification and high time complexity, which exist in tag quantity estimation algorithm in Radio Frequency IDentification (RFID) system, The Twice Labels Number Estimation algorithm based on Gaussian fitting and Chebyshev inequality (TLNEGC) is proposed. Firstly, a collision model is established based on the relationship between the collision factor and the collision time slot ratio, and a Gaussian estimation model is obtained by fitting the Gaussian function to the discrete data points. Afterward, the Gaussian estimation model is used to initially estimate the number of labels, and then according to the results of the initial estimation, judge whether a second estimation is required. The second estimation is performed by using Chebyshev's inequality to search the estimation interval twice to obtain the best estimate. The MATLAB simulation analysis indicates that the average estimation error and total time consumption of the TLNEGC algorithm are significantly lower than those of existing high-precision label estimation algorithms, and it also has lower time complexity and higher stability.
- Radio Frequency IDentification (RFID),
- Labels estimation,
- ALOHA algorithm,
- Chebyshev inequality,
- Gaussian fitting

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References(18)

References

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