基于机器学习算法的室内定位效率分析
Analysis of Indoor Positioning Efficiency Based on Machine Learning Algorithms
范伟龙1
作者信息
- 1. 广东能源集团科学技术研究院有限公司,广州 510630
- 折叠
摘要
阐述为有效减轻匹配计算复杂度,结合位置指纹室内定位技术特性,从缩小匹配区域、减少匹配维度、降低算法复杂度三个维度出发,提出PCA与K-means联合简化的机器学习算法,以优化定位性能.
Abstract
This paper describes a machine learning algorithm that combines PCA and K-means to effectively reduce the complexity of matching calculations,taking into account the characteristics of indoor positioning technology using location fingerprints.Starting from three dimensions:reducing the matching area,reducing the matching dimension,and reducing the algorithm complexity,the algorithm aims to optimize the positioning performance through the joint simplification of PCA and K-means.
关键词
室内定位/机器学习/K-means/主成分分析Key words
indoor positioning/machine learning/K-means/principal component analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据