【作者】 曹一鸣；

【导师】 朱卫平；

【作者基本信息】 南京邮电大学 ， 信号与信息处理， 2019， 硕士

【摘要】 随着无线电通信技术、移动互联网技术的发展,很多场合对于基于位置的服务(Location Based Services,LBS)的需求越来越高。然而应用广泛的卫星类定位系统由于受到建筑物的遮挡,室内定位效果差,因此,室内场景需要非全球导航卫星系统(Global Navigation Satellite System,GNSS)进行定位。室内定位研究近年来已经成为热点。由于室内高密度的Wi-Fi节点布设,Wi-Fi信号室内覆盖范围大。大量的移动终端都内嵌Wi-Fi接收机,因此Wi-Fi信号在室内容易接收。极限学习机(Extreme Learning Machine,ELM)作为一种新型的前馈神经网络(feed-forward neuron network)机器学习算法,其隐层节点参数可随机给定且不需人为调整,学习效率高,泛化能力强。因此本文开展基于ELM的Wi-Fi室内定位技术研究。研究主要包括以下几个方面:(1)研究基于接收信号强度指示(Received Signal Strength Indicator,RSSI)的指纹定位模型和工作原理,在此基础上介绍了常用的指纹匹配算法。然后对极限学习机室内定位的研究现状进行了分析,为后续研究工作打下基础。(2)针对大范围的定位需求,提出一种基于多核极限学习机(Multiple Kernel Extreme Learning Machine,MK-ELM)和迭代自组织数据分析(Iterative Self-organizing Data Analysis,ISODATA)聚类技术的定位算法。在离线阶段,通过迭代自组织数据分析聚类技术对接收信号强度测量值进行数据预处理,获得每一个接收信号强度测量值的标签,从而形成接收信号强度-标签训练集和根据不同标签划分的接收信号强度-位置坐标训练子集;然后使用多核极限学习机算法对上述训练数据集分别进行训练,得到测量值的分类函数和位置回归函数集。在线阶段,利用分类函数对接收到的信号强度测量值进行分类,然后根据分类结果选择相应的回归函数进行位置估计。所提算法利用无监督聚类的数据预处理技术,不需要任何关于测量信息的先验知识,操作简单。利用极限学习机算法训练速度极快,提高定位系统实时性,多核函数的使用保证定位精度。(3)针对大量RSSI测量值未标记的情况,提出一种新的基于极限学习机的RSSI指纹定位算法。在离线阶段,作为无监督聚类分析,利用迭代自组织数据分析技术揭示训练数据RSSI测量值的固有性质并获得每个RSSI数据的类别。然后,为了提高分类能力和鲁棒性,提出利用多个核函数的多核极限学习机算法进行RSSI测量值分类学习,获得更准确的RSSI测量分类函数。然后提出两阶段RSSI测量值特征提取算法。对于每个RSSI训练子集,首先在高维特征空间中进行核主成分分析(Kernel principal component analysis,KPCA),获得粗RSSI测量值特征。然后引入基于深度网络的极限学习机,获得RSSI测量值的精特征。最后,利用于半监督回归学习训练每个训练数据子集的RSSI测量值精特征,获得位置回归函数集合。在线阶段,将RSSI测量值进行分类,然后完成特征提取,利用相应的位置回归函数来估计目标位置。与单核学习相比,所提算法采用多核极限学习机算法具有更好的回归能力和鲁棒性。通过两阶段特征提取处理,所提算法能够获得更有效的测量值特征,从而提高离线学习效率。更多还原

【Abstract】 With the development of wireless telecommunication technology and mobile internet technology,location based services(LBS)becomes more and more important in many practical applications.However,satellite positioning system which has been widely applied does not perform well in indoor environments,because the signals can be blocked with the buildings.Thus,indoor positioning has become a hot research spot in recent years.Because of the high-density layout of the Wi-Fi node,the coverage of Wi-Fi signal in indoor environment is very large.Moreover,Wi-Fi receivers have been embedded into a large number of mobile terminals.Therefore,it is very easy to receive the Wi-Fi signals.As a new kind of feed-forward neuron network,the hidden node parameters of extreme learning machine(ELM)can be randomly given.As a result,ELM has high learning efficiency and a strong generalization ability.So,in this thesis,we will study the Wi-Fi based indoor positioning algorithms using the ELM technology.The research mainly includes the following aspects:(1)First,the fingerprint positioning model and working principle by the received signal strength indicator(RSSI)measurements have been studied.Then,traditional fingerprint matching methods are introduced.Next,some existing ELM based indoor localization algorithms are analyzed,which gives the foundation for follow-up research.(2)For the large-scale localization requirement,a new locational algorithm using multiple kernel extreme learning machine(MK-ELM)and iterative self-organizing data analysis(ISODATA)is proposed.In the off-line phase,the received RSSI measurements are pre-processed by the ISODATA technique and then the label of each RSSI measurement can be obtained.Thus,the RSSI-label training set and RSSI-position training subsets which are divided by different labels are formed.Then,the MK-ELM technique is used for both the classification learning and the regression learning by the above training data set.The RSSI measurement based classification function and position based regression functions are also obtained.In the on-line phase,after the RSSI measurement classification result of the received RSSI measurement,proper position based regression function is chosen for final position estimation.Because of the unsupervised clustering data pre-processing utilization,the proposed algorithm does not require the prior knowledge of measurement information and the operation is easy.Moreover,since the ELM technique and multiple kernel function are used for localization,the real-time capability and accuracy of the proposed algorithm can be improved.(3)A new indoor localization algorithm with received signal strength indicators fingerprints by extreme learning machine(ELM)technique is proposed,when the large amount of RSSI measurements are unlabeled.In the off-line phase,the ISODATA techniques algorithm method,as an unsupervised clustering analysis,is used to reveal the inherent nature of the RSSI measurement training data and obtain the class of each RSSI measurement.Then,in order to increase the classification capability and robustness,the multi-kernel ELM learning method which consider more than one kernel function,is proposed for the RSSI classification learning and obtain more accurate RSSI measurement classification function.Next,a two-stage RSSI measurement feature extraction algorithm is proposed.For each RSSI training subset.The kernel principal component analysis(KPCA)which can enable the linear operations in a high dimensional feature space is used to obtain coarse Wi-Fi feature at first.And then the deep learning network based ELM is introduced to get high level refined feature of RSSI measurement.At last,each training data subset with the refined RSSI feature is used for semi-supervised regression learning and obtain the position regression functions.In the on-line phase,after the RSSI measurement classification and feature extraction of the received RSSI measurements,the target position can be estimated with the corresponding position regression function.Compared with the single-kernel learning technique,the proposed algorithm has better regression capability and robustness.Moreover,the off-line learning performance of the proposed algorithm can be improved,since more efficient feature of the RSSI measurements can be obtained by the proposed two-stage feature extraction technique.更多还原