LANDMARC Localization Algorithm Based on Weight Optimization

HU Bing; PENG Huijun; SUN Zhixin

doi:10.1049/cje.2017.08.011

Volume 27 Issue 6

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HU Bing, PENG Huijun, SUN Zhixin, “LANDMARC Localization Algorithm Based on Weight Optimization,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1291-1296, 2018, doi: 10.1049/cje.2017.08.011

Citation:

HU Bing, PENG Huijun, SUN Zhixin, “LANDMARC Localization Algorithm Based on Weight Optimization,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1291-1296, 2018, doi: 10.1049/cje.2017.08.011

HU Bing, PENG Huijun, SUN Zhixin, “LANDMARC Localization Algorithm Based on Weight Optimization,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1291-1296, 2018, doi: 10.1049/cje.2017.08.011

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LANDMARC Localization Algorithm Based on Weight Optimization

doi: 10.1049/cje.2017.08.011

Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61373135, No.61672299), the Research Project of Jiangsu Province (No.BY2013011), and the Natural Science Foundation of Jiangsu Province of China (No.BK20140883).

More Information

Corresponding author: SUN Zhixin (corresponding author) was born in Anhui Province, China. He is now a professor and Ph.D. supervisor of the College of Internet of Things in Nanjing University of Posts and Telecommunications. His research interests include computer networks and security, multimedia communication, and mobile Internet. (Email:Sunzx@njupt.edu.cn)
Received Date: 2017-01-12
Rev Recd Date: 2017-03-03
Publish Date: 2018-11-10

Abstract

Abstract

In order to solve the problem that the difference of Received signal strength (RSS) between tags will become large when tags are close to the reader, which exists in LANDMARC system, a LANDMARC localization algorithm based on weight optimization is proposed in the paper. We optimize the weight by redefining the formulas of in-weight and ex-weight. In-weight formula is discussed under the free space propagation model. The definition of ex-weight is obtained by analyzing the relationship between RSS and distance under the log-distance path loss model. We evaluate the performance of the proposed algorithm and compare it with the algorithm based on normalized weight. The simulation results show the superior performance of the proposed algorithm in terms of location accuracy.

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References(20)

References

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