随着智能家居应用的不断深化,基于Wi-Fi信号的室内定位技术也受到了广泛关注.在实际应用中,大多数室内定位算法采集得到的训练数据和测试数据通常并非来自于同一理想环境,各种环境条件变化以及信号漂移导致采集得到的训练数据和测试数据间的概率分布不同.传统定位模型在面对不同分布的训练数据和测试数据时无法保证具有良好的定位精度,常出现算法定位精度大幅降低,甚至算法不可用等问题.面对这一难点,迁移学习中的域适应方法作为一种可以有效解决训练样本和测试样本概率分布不一致的学习问题被广泛应用于室内定位领域.文中结合域适应学习和机器学习算法,提出了 一种基于特征迁移的室内定位算法(Transfer Learning Location Algorithm Based on Global and Local Metrics Adaptation,TL-GL-MA).TL-GLMA在定位阶段通过特征迁移方式将两域原始数据映射至高维空间,从而在最小化两域数据的分布差异的同时保留两域数据内部的局部几何属性,并利用映射后的独立同分布数据训练分类器,从而实现目标定位.实验结果表明,TL-GL-MA能够有效减少环境变化带来的干扰,提升定位精度.
Indoor Location Algorithm in Dynamic Environment Based on Transfer Learning
With the development of smart home,the Wi-Fi signal-based localization technology has also been widely studied.In actual application,the training data and test data collected by indoor positioning algorithm usually do not come from the same ideal conditions.Changes in various environmental conditions and signal drift can cause different probability distributions between the training data and test data.The existing positioning algorithm cannot guarantee stable accuracy when facing these different probability distributions,resulting in dramatic reduction and infeasibility of the positioning accuracy of indoor location algo-rithms.Considering these difficulties,the domain adaptation technology in transfer learning is proven to be a promising solution in past researches to solve the inconsistent probability distributions problem.In this paper,a feature transferbased indoor localization algorithm TL-GLMA is proposed by combining domain adaptation learning and machine learning algorithms.TL-GLMA maps the original data of two domains to the high-dimension space through feature transfer,so as to minimize the distribution difference between the two domains in retaining the local geometric properties.In addition,because the mapped data is independent and iden-tically distributed,TL-GLMA can use it for training the classifier to achieve better location result.Experiment results show that TL-GLMA can effectively reduce the interference caused by environmental changes and improve the location accuracy.
万方数据
维普
