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Improvement of location positioning using KNN, Local Map Classification and Bayes Filter for indoor location recognition system

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(6), pp.29-35
  • DOI : 10.9708/jksci.2021.26.06.029
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 18, 2021
  • Accepted : May 31, 2021
  • Published : June 30, 2021

Seung-Hoon Oh 1 MAENG JUHYUN 2

1고려대학교
2한양대학교

Accredited

ABSTRACT

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

Citation status

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