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Enhanced signal propagation models and algorithm selector for providing location estimation services within cellular radio networks.

机译：用于在蜂窝无线网络内提供位置估计服务的增强型信号传播模型和算法选择器。

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摘要

Mobile location estimation or mobile positioning is a crucial technology for mobile computing and ubiquitous computing. In this research, our purpose is to provide the estimation of the location of the mobile station (MS) under the GSM network, which is one of the dominant cellular radio networks in Hong Kong. Furthermore, our designs of models and algorithms are required to apply to different kinds of cellular radio networks, like the CDMA network, for real applications. We have designed our location models and algorithms based on the common attribute of all cellular radio networks---the Received Signal Strength (RSS). So our proposed models and algorithms in the thesis can be applied to all kinds of cellular radio networks in theory.; A geometric model, the Ellipse Propagation Model (EPM), has been proposed to provide the estimation of the location of the MS. It is a geometric model which considers the directional transmission property of the antenna. We present two algorithms based on EPM: the Geometric Algorithm and the Iterative Algorithm. We also present a data fusion method, the Statistical Estimation, with EPM to estimate the location of the MS. It uses the information of more than one snapshot to provide the estimation of the location of the MS. It can reduce the effect of the signal fading and fluctuation to the estimation, thus, it can provide an accurate estimation for location services. We then extend this geometric model into a 3D space in order to provide a 3D estimation of the location of the MS in applications.; Although EPM is simple and efficient to provide the estimation of the location of the MS, it is a simple approximate relationship between the RSS and the MS-BS distance. EPM is too simple to describe the complex surroundings factors. In view of that, we present a probabilistic model, the Modified Directional Propagation Model (MDPM), to describe the relationship between the RSS and the MS-BS distance. MDPM is derived from the Directional Propagation Model (DPM), and combines the merits of DPM, EPM and SPM---the Statistical Propagation Model. We also propose an iterative method, not Expectation-Maximization (EM) algorithm, to provide the estimation of the model parameter, which can reach the global maximum of the likelihood function about the model parameter. We then provide the estimation of the location of the MS based on MDPM with the Bayes Estimation of the model parameter. Although our experiment provides a 2D solution in this thesis, our method of MDPM can be easily extended to the 3D space. Namely, we can provide a 3D estimation the location of the MS with our method without any major changes.; After we have presented a geometric model, EPM, and a probabilistic model, MDPM, it is natural to combine these models and gets the best out of these algorithms, since each algorithm mentioned in this thesis has its own advantage over different regions. We have presented three algorithm selectors making use of the LDA Classifier Model and the Bayes Classifier Models to combine the merits of these models and algorithms we have proposed in our previous work. For the Bayes Classifier Models, we propose two variations: the Naive Bayes Probabilistic Model and the Bayes Probabilistic Model.; With the technical support from the local mobile phone operator, we have constructed and conducted several real world experiments in different kinds of environments in Hong Kong for our investigation. Experimental results show that the algorithm selector is effective and can provide an accuracy better than by any single algorithm alone in all kinds of terrains and environments in Hong Kong.
机译：移动位置估计或移动定位是移动计算和普适计算的关键技术。在这项研究中，我们的目的是提供对GSM网络下移动站（MS）位置的估计，该网络是香港主要的蜂窝无线网络之一。此外，我们的模型和算法设计需要应用于实际应用中的各种类型的蜂窝无线网络（如CDMA网络）。我们根据所有蜂窝无线网络的共同属性-接收信号强度（RSS）设计了位置模型和算法。因此，本文提出的模型和算法在理论上可以应用于各种蜂窝无线网络。已经提出了几何模型，椭圆传播模型（EPM）来提供对MS位置的估计。它是一个几何模型，考虑了天线的定向传输特性。我们提出了两种基于EPM的算法：几何算法和迭代算法。我们还提出了一种具有EPM的数据融合方法，即统计估计，以估计MS的位置。它使用多个快照的信息来提供对MS位置的估计。它可以减少信号衰落和波动对估计的影响，从而可以为定位服务提供准确的估计。然后，我们将此几何模型扩展到3D空间中，以便对应用程序中MS的位置提供3D估计。尽管EPM简单有效地提供了MS的位置估计，但它是RSS与MS-BS距离之间的简单近似关系。 EPM太简单，无法描述复杂的环境因素。有鉴于此，我们提出了一种概率模型，即修正方向传播模型（MDPM），用于描述RSS与MS-BS距离之间的关系。 MDPM源自定向传播模型（DPM），并结合了DPM，EPM和SPM的优点-统计传播模型。我们还提出了一种迭代方法，而不是期望最大化（EM）算法，以提供模型参数的估计，该估计可以达到关于模型参数的似然函数的全局最大值。然后，我们基于模型参数的贝叶斯估计，基于MDPM提供MS位置的估计。尽管我们的实验在本文中提供了2D解决方案，但我们的MDPM方法可以轻松扩展到3D空间。即，我们可以使用我们的方法提供3D估计MS的位置，而无需进行任何重大更改。在介绍了几何模型EPM和概率模型MDPM之后，很自然地将这些模型组合起来并从这些算法中获得最大的收益，因为本文中提到的每种算法在不同地区都有自己的优势。我们介绍了三个使用LDA分类器模型和贝叶斯分类器模型的算法选择器，以结合我们先前工作中提出的这些模型和算法的优点。对于贝叶斯分类器模型，我们提出了两种变体：朴素贝叶斯概率模型和贝叶斯概率模型。在本地手机运营商的技术支持下，我们在香港的不同环境中构建并进行了多个真实世界的实验，以供我们调查。实验结果表明，该算法选择器在香港各种地形和环境中均有效，并且比单独使用任何一种算法都能提供更好的精度。

著录项

  • 作者

    Zhou, Junyang.;

  • 作者单位

    Hong Kong Baptist University (Hong Kong).;

  • 授予单位 Hong Kong Baptist University (Hong Kong).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 173 p.
  • 总页数 173
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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