• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Conference on Local Computer Networks >On the Dimension Reduction of Radio Maps with a Supervised Approach
【24h】

On the Dimension Reduction of Radio Maps with a Supervised Approach

机译：有监督方法的无线电地图降维

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Radio maps play a vital role in fingerprint-based indoor positioning systems (IPSs) in terms of the localization accuracy and computational overheads. Most existing studies either directly eliminate redundant APs or adopt unsupervised dimension reduction methods, say principal component analysis (PCA), to obtain a low-dimension representation of fingerprints, which consumes less storage and computational overheads. In this paper, we propose to reduce the dimensions of radio maps based on the Gaussian Process Manifold Kernel Dimension Reduction (GPMKDR) which is a supervised dimension reduction technique in comparison with the well known PCA-based method. Specifically, GPMKDR is employed to find a nonlinear and optimal embedding into the received signal strength (RSS) sample space during the offline phase, such that any RSS sample vector obtained in the online localization phase can be projected onto the optimal subspace with a lower dimension, with the result that the fingerprint-based localization can be efficiently realized based on a low-dimension radio map. Experiments show that the nonlinear GPMKDR-based method significantly improves the localization performance in comparison with the PCA-based method.
机译：就定位精度和计算开销而言，无线电地图在基于指纹的室内定位系统（IPS）中起着至关重要的作用。现有的大多数研究要么直接消除冗余的AP，要么采用无监督的降维方法（例如主成分分析（PCA））来获取指纹的低维表示形式，从而减少了存储和计算开销。在本文中，我们建议基于高斯过程流形内核维数缩减（GPMKDR）来减少无线电图的维数，该方法是与众所周知的基于PCA的方法相比的一种有监督的维数缩减技术。具体而言，GPMKDR用于在离线阶段找到非线性的最优嵌入到接收信号强度（RSS）样本空间的方法，这样，在线定位阶段获得的任何RSS样本矢量都可以投影到具有较低维的最优子空间上，结果是基于低维无线电图可以有效地实现基于指纹的定位。实验表明，与基于PCA的方法相比，基于GPMKDR的非线性方法显着提高了定位性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号