几个镜头学习WiFi指纹室内定位
Zhenjie Ma1, Ke Shi1
1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了一种新的基于网络的定位系统,使用了WiFi指纹的几次拍摄学习. 它以最少的数据实现了强大的室内定位,克服了信号波动并减少了人工精力.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 信号处理 信号处理
背景情况:
- 基于深度学习的WiFi指纹识别提供了准确的室内位置服务.
- 现有的方法需要进行广泛的现场调查,并应对信号波动.
研究的目的:
- 为了开发一个强大的WiFi指纹系统,使用少数镜头学习.
- 为了减少对大型标记数据集的需求,并减轻信号不稳定性.
主要方法:
- 一个原型的基于网络的定位系统,利用几次拍摄的学习.
- 时间卷积网络 (TCN) 用于样本嵌入和质量评估.
- 卡尔曼波器用于解决来自信号波动的空间模糊性.
主要成果:
- 拟议的系统建立了一个强大的RSSI位置匹配模型,具有有限的标签.
- 与使用更少标记样本的现有深度学习模型相比,实现了更高的性能.
- 在减轻信号波动引起的空间模两可的方面表现出有效性.
结论:
- 少数拍摄的学习方法显著提高了WiFi指纹的效率和稳定性.
- 该系统提供了精确的室内定位,减少了数据和提高了对信号变化的弹性.
- 这种方法为实际的室内定位系统提供了一个有希望的替代方案.
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