在室内物联网时代,使用深度学习算法用于Wi-Fi指纹定位技术的新型集成匹配算法.
Safar Maghdid Asaad1,2, Halgurd Sarhang Maghdid2
1Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq.
PeerJ. Computer science
|June 22, 2023
概括
这项研究介绍了Norm_MSATE_LSTM,这是一种改进室内Wi-Fi定位精度的新算法. 该方法增强了接收信号强度指标 (RSSI) 数据,大大提高了物联网 (IoT) 应用的定位性能.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 无处不在的计算无处不在的计算
背景情况:
- 物联网 (IoT) 设备越来越多地使用Wi-Fi跟踪室内活动.
- 基于接收信号强度指标 (RSSI) 的室内定位面临诸如多路径,非视线 (NLOS) 问题和数据波动等挑战.
- 现有的指纹采集方法在数据样本不足和不稳定的匹配方面扎,特别是在复杂的环境中.
研究的目的:
- 提出一种新的匹配算法,Norm_MSATE_LSTM,以提高室内Wi-Fi定位精度.
- 解决基于RSSI的指纹识别的局限性,包括数据稀缺性和匹配不稳定性.
- 为了提高物联网应用室内定位的可靠性和精度.
主要方法:
- 实施了平均标准偏差增大技术 (MSATE) 用于数据增大以增加RSSI记录.
- 应用数据规范化 (规范) 到RSSI记录.
- 使用长短期内存 (LSTM) 网络进行位置估计.
- 将拟议的Norm_MSATE_LSTM算法与加权k-最近邻居 (WkNN) 和独立的LSTM进行了比较.
主要成果:
- 提出的Norm_MSATE_LSTM算法在定位准确度方面取得了显著的改进.
- 仅仅通过增强,精度提高了33.1%.
- 精度提高了57.5%,增强和正常化相结合.
- 使用OMNeT++的实验和模拟结果验证了算法的有效性.
结论:
- 该Norm_MSATE_LSTM算法有效地减轻了基于RSSI的指纹在室内定位方面的问题.
- 数据增强和规范化,结合LSTM,大大提高了本地化准确性.
- 拟议的方法为物联网系统中可靠的室内跟踪提供了一个有希望的解决方案.
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