Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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RFID Indoor Location Recognition with Obstacle Using Neural Network

신경망을 이용한 장애물이 있는 RFID 실내 위치 인식

  • Lee, Jong-Hyun (Department of Electronic Engineering, Incheon National University) ;
  • Lee, Kang-bin (Department of Electronic Engineering, Incheon National University) ;
  • Hong, Yeon-chan (Department of Electronic Engineering, Incheon National University)
  • Received : 2018.07.24
  • Accepted : 2018.10.07
  • Published : 2018.11.30
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Abstract

Since the indoor location recognition system using RFID is a method for predicting the indoor position, an error occurs due to the surrounding environment such as an obstacle. In this paper, we plan to reduce errors using back propagation neural networks. The neural network adjusts and trains the connection values between the layers to reduce the error between the actual position of the object with the reader and the expected position of the object through the experiment. In this paper, we propose a method that uses the median method and the radiation method as input to the neural network. Among the two methods, we want to find out which method is more effective in recognizing the actual position in an environment with obstacles and reduce the error. Consequently, the method using the median has less error, and we confirmed that the more the number of data, the smaller the error.

RFID를 이용한 실내 위치 인식 시스템은 실내의 위치를 예측하는 방식이기 때문에 장애물 등 주변 환경에 의해 오차가 발생한다. 본 논문에서는 역전파 신경망을 이용하여 오차를 줄이고자 한다. 신경망은 층간의 가중치를 조정하고 훈련시켜 리더를 보유한 물체의 실제위치와 실험을 통해 예상되는 위치간의 오차를 줄인다. 본 논문에서는 중앙값을 사용한 방법과 방사 형태를 사용한 방법을 신경망의 입력으로 사용하는 구성을 제안하였다. 두 가지 방법 중 장애물이 있는 환경에서 어떤 방법이 실제 위치를 인식하는 데에 더 효율적인지 확인하고 오차를 줄이고자 한다. 그 결과 중앙값을 이용한 방법이 오차가 더 적었으며, 데이터 개수가 많을수록 오차가 더 줄어드는 것을 확인하였다.

Keywords

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Fig. 1 Structure of the back propagation neural network.

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Fig. 2 Method of obtaining the median value.

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Fig. 3 Median value of a reader and read tags.

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Fig. 4 Compensation for tags which are not included in the radiation pattern.

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Fig. 5 Arrangement of a reader, tags, and an obstacle.

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Fig. 6 Alien RFID Gateway program.

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Fig. 7 Comparison of mean errors of radiation pattern and median value.

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Fig. 8 Comparison of mean errors of median value and median value with neural network.

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Fig. 9 Comparison of mean errors of radiation pattern and radiation pattern with neural network.

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Fig. 10 Comparison of mean errors of radiation pattern with neural network and median value with neural network.

Table. 1 Number of training and test data.

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