论文标题
ML使用RSSI在WiFi 2.4 GHz频带上进行位置预测
ML for Location Prediction Using RSSI On WiFi 2.4 GHZ Frequency Band
论文作者
论文摘要
几十年来，使用不同的技术实现了对象位置的确定。尽管GPS（全球定位系统）提供了可扩展的有效且具有成本效益的位置服务，但是卫星发射的信号不能在室内开发以有效地确定位置。与GP相反，GP是室外位置的一种经济有效的本地化技术，已经研究了几种用于室内定位的技术。其中包括无线保真度（Wi-Fi）蓝牙低能（BLE）和接收的信号强度指示器（RSSI）等。本文提出了一种使用RSSI用作通过应用一些机器学习（ML）概念来确定对象位置的均值的增强方法。二进制分类是通过考虑表示对象位置的坐标的邻接来定义的。提出的特征通过多个分类器进行经验测试，该分类器的最高准确度达到了96％。
For decades, the determination of an objects location has been implemented utilizing different technologies. Despite GPS (Global Positioning System) provides a scalable efficient and cost effective location services however the satellite emitted signals cannot be exploited indoor to effectively determine the location. In contrast to GPS which is a cost effective localization technology for outdoor locations several technologies have been studied for indoor localization. These include Wireless Fidelity (Wi-Fi) Bluetooth Low Energy (BLE) and Received Signal Strength Indicator (RSSI) etc. This paper presents an enhanced method of using RSSI as a mean to determine an objects location by applying some Machine Learning (ML) concepts. The binary classification is defined by considering the adjacency of the coordinates denoting objects locations. The proposed features were tested empirically via multiple classifiers that achieved a maximum of 96 percent accuracy.
