[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2021.26.06.029

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

Oh, Seung-Hoon (Dept. of Computer Science, Korea University)
Maeng, Ju-Hyun (Dept. of Software Engineering, Hanyang University)

Publication Information

Journal of the Korea Society of Computer and Information / v.26, no.6, 2021 , pp. 29-35 More about this Journal

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.

Keywords

Positioning; KNN; K-Mean; Bayes Filter; Mobile Robot;

Citations & Related Records

Reference

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