Ma Zhenjie, Shi Ke
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2023 Oct 13;23(20):8458. doi: 10.3390/s23208458.
In recent years, deep-learning-based WiFi fingerprinting has been intensively studied as a promising technology for providing accurate indoor location services. However, it still demands a time-consuming and labor-intensive site survey and suffers from the fluctuation of wireless signals. To address these issues, we propose a prototypical network-based positioning system, which explores the power of few-shot learning to establish a robust RSSI-position matching model with limited labels. Our system uses a temporal convolutional network as the encoder to learn an embedding of the individual sample, as well as its quality. Each prototype is a weighted combination of the embedded support samples belonging to its position. Online positioning is performed for an embedded query sample by simply finding the nearest position prototype. To mitigate the space ambiguity caused by signal fluctuation, the Kalman Filter estimates the most likely current RSSI based on the historical measurements and current measurement in the online stage. The extensive experiments demonstrate that the proposed system performs better than the existing deep-learning-based models with fewer labeled samples.
近年来，基于深度学习的WiFi指纹识别技术作为一种有望提供精确室内定位服务的技术得到了深入研究。然而，它仍然需要耗时且费力的现场勘测，并且受到无线信号波动的影响。为了解决这些问题，我们提出了一种基于原型网络的定位系统，该系统利用少样本学习的能力，在标签有限的情况下建立一个强大的接收信号强度指示（RSSI）-位置匹配模型。我们的系统使用时间卷积网络作为编码器，以学习单个样本的嵌入表示及其质量。每个原型是属于其位置的嵌入支持样本的加权组合。通过简单地找到最近的位置原型，对嵌入的查询样本进行在线定位。为了减轻信号波动引起的空间模糊性，卡尔曼滤波器在在线阶段根据历史测量值和当前测量值估计最可能的当前RSSI。大量实验表明，所提出的系统在标记样本较少的情况下，性能优于现有的基于深度学习的模型。
