基于改进对比学习和并行融合神经网络的室内 WiFi 定位算法

机器学习在WiFi指纹定位技术中扮演着重要角色。针对信号波动对指纹辨识力的影响往往被忽略以及如何从样本中提取更广泛的表征信息的问题，提出了一种基于改进对比学习(CL)和并行融合神经网络的WiFi定位算法。该算法首先利用改进对比学习来提高指纹辨识力，其在增加不同类别指纹间的区分度的同时能减小同类别指纹间的差异。其次，构建基于卷积神经网络(CNN)和长短期记忆(LSTM)的并行融合网络，与传统的串行融合方式相比，网络可以从原始样本中提取更多的有效特征。此外，在池化层后增加Flatten层以进一步考虑网络的中间层信息，从而利用更广泛的特征信息来提高模型的泛化性能。结果表明，所提算法的定位性能比其他定位算法提高26%。

Indoor WiFi localization algorithm based on the improved contrastive learning and parallel fusion neural network

Machine learning plays an important role in WiFi fingerprint localization techniques. To address the problem that the effect of signal fluctuation on fingerprint recognition is often ignored and how to extract broader representation information from samples, this article proposes a WiFi localization algorithm based on improved contrastive learning and parallel fusion neural network. Firstly, the algorithm utilizes the improved CL to improve fingerprint discrimination, which increases the differentiation between different categories of fingerprints while reducing the differences between fingerprints of the same category. Secondly, a parallel fusion network based on CNN and LSTM is established. Compared with the traditional serial fusion method, the network can extract more effective features from the original samples. In addition, a flatten layer is added after the pooling layer to further consider the intermediate layer information of the network. Thus, a wider range of feature information is utilized to improve the generalization performance of the model. The results show that the proposed algorithm improves the localization performance by 26% over other localization algorithms.