摘要
针对室内复杂环境下无线信号不稳定、传统支持向量机定位算法计算复杂度高等难题,为了提高室内的定位精度,提出一种改进支持向量机的Wi-Fi室内定位算法。采用核主成分分析对特征进行降维处理,提取有用信息、降低计算量,采用支持向量机构建定位特征与物理位置的非线性映射模型,并采用粒子群算法对模型参数进行优化,进行了仿真实验。结果表明,该算法提高了室内定位精度和效率。
Wi-Fi signal is unstable in complex indoor environment and localization precision of support vector machine is very low. In order to improve the localization precision of indoor nodes, a novel indoor localization algorithm is proposed based on improved support vector machine. The kernel principal component analysis is used to extract useful information and obtain the features which reduce the computing complexity, and then support vector machine is used to construct nonlinear mapping localization model between features and physical location, in which parameters are optimized by particle swarm optimization algorithm. The simulation experiments are used to test the performance. The results show that the proposed algorithm has improved localization precision and efficiency for indoor localization.
出处
《计算机工程与应用》
CSCD
北大核心
2016年第6期90-93,共4页
Computer Engineering and Applications
基金
陕西省教育厅项目(No.20130031312)
关键词
室内定位
支持向量机
核主成分分析
粒子群优化算法
indoor localization
support vector machine
kernel principal component analysis
particle swarm optimization algorithm
