TY - GEN
T1 - Multi-floor Positioning Method based on RSSI in Wireless Sensor Networks
AU - Zhang, Huizi
AU - Fukunaga, Hayato
AU - Ishizuka, Ryo
AU - Li, Tong
AU - Tateno, Shigeyuki
N1 - Publisher Copyright:
© 2022 The Society of Instrument and Control Engineers - SICE.
PY - 2022
Y1 - 2022
N2 - Recently, with the rapid development of wireless communication technologies, indoor positioning has been gradually applied to many occasions, such as positioning in complex multi-floor shopping malls. In the positioning of shopping malls, the Wi-Fi fingerprint positioning method is often used. Moreover, k-nearest neighbor algorithm is commonly used to estimate the position of the target point in some studies. However, the k-nearest neighbor algorithm searched in the database of the whole building, resulting in low accuracy of floor determination, large positioning errors, and large amount of calculation. To solve this problem, the database of a building should be reasonably divided into several clusters, and the clusters with the highest similarity to the target point should be used for positioning. Therefore, in this paper, a building fingerprint database clustering method based on signal distance and position distance is proposed. The results show that compared with the k-nearest neighbor algorithm, the floor determination accuracy is improved, and the positioning error is reduced.
AB - Recently, with the rapid development of wireless communication technologies, indoor positioning has been gradually applied to many occasions, such as positioning in complex multi-floor shopping malls. In the positioning of shopping malls, the Wi-Fi fingerprint positioning method is often used. Moreover, k-nearest neighbor algorithm is commonly used to estimate the position of the target point in some studies. However, the k-nearest neighbor algorithm searched in the database of the whole building, resulting in low accuracy of floor determination, large positioning errors, and large amount of calculation. To solve this problem, the database of a building should be reasonably divided into several clusters, and the clusters with the highest similarity to the target point should be used for positioning. Therefore, in this paper, a building fingerprint database clustering method based on signal distance and position distance is proposed. The results show that compared with the k-nearest neighbor algorithm, the floor determination accuracy is improved, and the positioning error is reduced.
KW - Wi-Fi fingerprint positioning
KW - clustering based on signal distance and position distance
KW - multi-floor positioning
UR - http://www.scopus.com/inward/record.url?scp=85141191903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141191903&partnerID=8YFLogxK
U2 - 10.23919/SICE56594.2022.9905776
DO - 10.23919/SICE56594.2022.9905776
M3 - Conference contribution
AN - SCOPUS:85141191903
T3 - 2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
SP - 270
EP - 275
BT - 2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
Y2 - 6 September 2022 through 9 September 2022
ER -