Incremental Localization Algorithm Based on Regularized Iteratively Reweighted Least Square

• Autorzy: Yan X. , Yang Z. , Liu Y. , Xu X. , Li H.

Identyfikatory

DOI

10.1515/fcds-2016-0011

• Języki publikacji: EN

Abstrakty

EN

Incremental localization algorithm is a distributed localization method with excellent characteristics for wireless network. However, its estimated result is generally influenced by the heteroscedasticity arising from cumulative errors and the collineation among anchor nodes. We have proposed a novel incremental localization algorithm with consideration to cumulative errors and collinearity among anchors. Using iteratively reweighted and regularized method, the algorithm reduces the influences of errors accumulation and avoids collinearity problem between anchors. Simulation experiment results show that compared with the previous incremental localization algorithms, the proposed algorithm obtains a localization solution which not only has high accuracy but also high stability. Therefore, the proposed algorithm is suitable for different deployment environments and has high adaptability.

Słowa kluczowe

EN

wireless network; incremental localization; regularized iteratively reweighted least square; heteroscedasticity

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2016
• Tom: Vol. 41, No. 3
• Strony: 183--196
• Opis fizyczny: Bibliogr. 28 poz., fig., tab.

Twórcy

autor

Yan X.

• School of Intelligence Science and Control Engineering, Jinling Institute of Technology, Nanjing, China
• Remote Measurement and Control Key Lab of Jiangsu Province, Nanjing, China

autor

Yang Z.

• School of Intelligence Science and Control Engineering, Jinling Institute of Technology, Nanjing, China

autor

Liu Y.

• School of Computer Engineering, Jinling Institute of Technology, Nanjing, China

autor

Xu X.

• School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an, China

autor

Li H.

• School of Instrument Science and Engineering, Southeast University, Nanjing, China
• Remote Measurement and Control Key Lab of Jiangsu Province, Nanjing, China

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Uwagi

This is an extended version of the paper presented at the International Conference on Big Data Intelligence and Computing (DataCom 2015), Chengdu, China, December 19-21, 2015.

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.