Hidden Markov Model Based Localization Using Array Antenna

Inatomi, Yusuke; Hong, Jihoon; Ohtsuki, Tomoaki

doi:10.1007/s10776-013-0211-y

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…We have proposed a monitoring system which uses an antenna array at the receiver side, referred to as array sensor [7,8,6,10,4,13,3,5,9]. The system uses the change of signal subspace spanned by signal eigenvectors of covariance matrix from array sensor.…”

Section: Introductionmentioning

confidence: 99%

Ambient intelligence sensing using array sensor

Hong

Ohtsuki

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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In this paper we introduce a novel device-free radio based activity recognition with localization method with various applications, such as e-Healthcare and security. Our method uses the properties of the signal subspace, which are estimated using signal eigenvectors of the covariance matrix obtained from an antenna array (array sensor) at the receiver side. To classify human activities (e.g., standing and moving) and/or positions, we apply a machine learning method with support vector machines (SVM). We compare the classification accuracy of the proposed method with signal subspace features and received signal strength (RSS). We analyze the impact of antenna deployment on classification accuracy in non-line-of-sight (NLOS) environments to prove the effectiveness of the proposed method. In addition, we compare our classification method with k-Nearest Neighbor (KNN). The experimental results show that the proposed method with signal subspace features provides accuracy improvements over the RSS-based method.

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Section: Introductionmentioning

confidence: 99%

Ambient intelligence sensing using array sensor

Hong

Ohtsuki

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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Anchor of trust: towards collusion-resistant trusted indoor location for enterprise and industrial use

Biehl

Lee

Filby

2019

Pers Ubiquit Comput

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A Smart City Based on Ambient Intelligence

Ohtsuki

2017

IEICE Trans. Commun.

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SUMMARYThe United Nations (UN) reports that the global population reached 7 billion in 2011, and today, it stands at about 7.3 billion. This dramatic increase has been driven largely by the extension of people's lifetime. The urban population has been also increasing, which causes a lot of issues for cities, such as congestion and increased demand for resources, including energy, water, sanitation, education, and healthcare services. A smart city has been expected a lot to solve those issues. The concept of a smart city is not new. Due to the progress of information and communication technology (ICT), including the Internet of Things (IoT) and big data (BD), the concept of a smart city has been being realized in various aspects. This paper introduces the concept and definition of a smart city. Then it explains the ambient intelligence that supports a smart city. Moreover, it introduces several key components of a smart city. key words: a smart city, ambient intelligence, information and communication technology (ICT), Internet of things (IoT)

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Hidden Markov Model Based Localization Using Array Antenna

Cited by 3 publications

References 8 publications

Ambient intelligence sensing using array sensor

Ambient intelligence sensing using array sensor

Anchor of trust: towards collusion-resistant trusted indoor location for enterprise and industrial use

A Smart City Based on Ambient Intelligence

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