Optimization algorithm of K-means fingerprint location
K-means fingerprint localization can reduce the complexity of localization, and improving the real-time of location has become a hot-spot of current localization algorithm. However, the randomness of clustering has resulted in great instability to the localization. In this paper, two-step clustering...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2018-02-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000077862 |
| _version_ | 1818363802530349056 |
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| author | Yu Chengbo Li Caihong Zeng Liang |
| author_facet | Yu Chengbo Li Caihong Zeng Liang |
| author_sort | Yu Chengbo |
| collection | DOAJ |
| description | K-means fingerprint localization can reduce the complexity of localization, and improving the real-time of location has become a hot-spot of current localization algorithm. However, the randomness of clustering has resulted in great instability to the localization. In this paper, two-step clustering algorithm is proposed to optimize the clustering number according to the AIC criterion. Considering the nearest neighbor algorithm would result in great error, correlation coefficient method is used to determine the highest similarity of the sub-library, and estimate the final position. The experimental results show that the optimized algorithm improves not only the positioning accuracy, but also the real-time and stability of localization. |
| first_indexed | 2024-12-13T21:54:16Z |
| format | Article |
| id | doaj.art-3fbb9bb1b24c4c8aa016ef86602b753c |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-12-13T21:54:16Z |
| publishDate | 2018-02-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-3fbb9bb1b24c4c8aa016ef86602b753c2022-12-21T23:30:12ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982018-02-01442707410.16157/j.issn.0258-7998.1717673000077862Optimization algorithm of K-means fingerprint locationYu Chengbo0Li Caihong1Zeng Liang2Institute of Remote Test and Control，Chongqing University of Technology，Chongqing 400054，ChinaInstitute of Remote Test and Control，Chongqing University of Technology，Chongqing 400054，ChinaInstitute of Remote Test and Control，Chongqing University of Technology，Chongqing 400054，ChinaK-means fingerprint localization can reduce the complexity of localization, and improving the real-time of location has become a hot-spot of current localization algorithm. However, the randomness of clustering has resulted in great instability to the localization. In this paper, two-step clustering algorithm is proposed to optimize the clustering number according to the AIC criterion. Considering the nearest neighbor algorithm would result in great error, correlation coefficient method is used to determine the highest similarity of the sub-library, and estimate the final position. The experimental results show that the optimized algorithm improves not only the positioning accuracy, but also the real-time and stability of localization.http://www.chinaaet.com/article/3000077862fingerprint localizationK-meansAIC criteriontwo-step clusteringcorrelation coefficient method |
| spellingShingle | Yu Chengbo Li Caihong Zeng Liang Optimization algorithm of K-means fingerprint location Dianzi Jishu Yingyong fingerprint localization K-means AIC criterion two-step clustering correlation coefficient method |
| title | Optimization algorithm of K-means fingerprint location |
| title_full | Optimization algorithm of K-means fingerprint location |
| title_fullStr | Optimization algorithm of K-means fingerprint location |
| title_full_unstemmed | Optimization algorithm of K-means fingerprint location |
| title_short | Optimization algorithm of K-means fingerprint location |
| title_sort | optimization algorithm of k means fingerprint location |
| topic | fingerprint localization K-means AIC criterion two-step clustering correlation coefficient method |
| url | http://www.chinaaet.com/article/3000077862 |
| work_keys_str_mv | AT yuchengbo optimizationalgorithmofkmeansfingerprintlocation AT licaihong optimizationalgorithmofkmeansfingerprintlocation AT zengliang optimizationalgorithmofkmeansfingerprintlocation |