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논문 기본 정보

자료유형
학술저널
저자정보
Insoo Sohn (Dongguk University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 논문지 제어로봇시스템학회 논문지 제21권 제4호
발행연도
2015.4
수록면
378 - 384 (7page)

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초록· 키워드

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Indoor localization is becoming one of the most important technologies for smart mobile applications with different requirements from conventional outdoor location estimation algorithms. Fingerprinting location estimation techniques based on neural networks have gained increasing attention from academia due to their good generalization properties. In this paper, we propose a novel location estimation algorithm based on an ensemble of multiple neural networks. The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its" inaccuracy, incompleteness, and ambiguity. To the best of our knowledge, this work is the first to enhance the location estimation accuracy in indoor wireless environments based on a neural network ensemble using fingerprinting training data. To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high capacity WLAN technologies in indoor environments with multiple transmit and multiple receive antennas. The numerical results show that the proposed method based on the NNE technique outperforms the conventional methods and achieves very accurate estimation results even in environments with a low number of APs.

목차

Abstract
Ⅰ. INTRODUCTION
Ⅱ. CHANNEL MODEL
Ⅲ. NEURAL NETWORK BASED FINGERPRINTING LOCALIZATION
Ⅳ. NUMERICAL EVALUATIONS
Ⅴ. CONCLUSIONS
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UCI(KEPA) : I410-ECN-0101-2016-569-001311407