• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2019 IEEE International Symposium on Measurements amp; Networking >Indoor Location Services through Multi-Source Learning-based Radio Fingerprinting Techniques
【24h】

Indoor Location Services through Multi-Source Learning-based Radio Fingerprinting Techniques

机译：通过基于多源学习的无线指纹技术进行室内定位服务

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Proximity advertising, smart parking and tourism are just examples of use-cases of location-based services that have become extremely popular in the last few years, also thanks to the pervasive diffusion of GNSS-enabled mobile devices. These devices, however, are not able guarantee adequate accuracy in indoor scenarios, that represent the actual frontier of next-generation location-based services. To this aim, we present in this paper Wireless Locator (WI-LO), a novel framework for the indoor localization of smartphone devices and the automation of location-based tasks. Through the WI-LO Web portal, users can import an indoor planimetry, set the Reference Points (RPs), and define the actions to execute at each RP or region or RPs. The WI-LO localization engine implements hybrid Radio Finger-Printing (RF) techniques, and it leverages on a variety of sensors embedded in Commercial Off The Shelf (COTS) smartphones (Wi-Fi, BLE, LTE, magnetometer). We investigate the utilization of Machine Learning (ML) techniques for the processing of the radio fingerprints of each source, and the application of fusion policies in order to aggregate the hard-decisions of each source. The evaluation analysis, conducted at the DISI@UNIBO department, confirms the ability of the WI-LO platform to deliver geo-fencing messages with over 90% accuracy, and it investigates the impact of different ML techniques, application parameters and scenario settings on the overall localization performance.
机译：邻近广告，智能停车和旅游业只是基于位置的服务用例的例子，在过去几年中，基于位置的服务已变得非常流行，这也要归功于启用GNSS的移动设备的广泛普及。但是，这些设备无法在室内场景中保证足够的精度，而这些场景代表了下一代基于位置的服务的实际前沿。为此，我们在本文中介绍了无线定位器（WI-LO），这是一种用于智能手机设备的室内定位和基于位置的任务的自动化的新颖框架。通过WI-LO Web门户，用户可以导入室内平面图，设置参考点（RP）并定义要在每个RP或区域或RP上执行的动作。 WI-LO本地化引擎实现了混合的无线电指纹（RF）技术，并利用了嵌入在商用（COTS）智能手机中的各种传感器（Wi-Fi，BLE，LTE，磁力计）。我们研究了利用机器学习（ML）技术处理每个来源的无线电指纹，以及融合策略的应用，以便汇总每个来源的硬性决定。由DISI @ UNIBO部门进行的评估分析证实了WI-LO平台能够以超过90％的准确度传递地理围栏消息的能力，并且调查了不同的机器学习技术，应用程序参数和场景设置对网络的影响。整体本地化性能。

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