针对基于指纹的室内定位在离线阶段指纹点数量较少以及指纹特征缺乏代表性，导致定位性能不佳的问题，提
出了基于反距离加权(Inverse Distance Weighted，IDW)插值和幅相融合网络的信道状态信息(Channel State Information，CSI)室
内定位方法。采用IDW插值算法生成大容量的指纹库；然后用并行卷积神经网络(convolutional neural network，CNN)处理振
幅和相位，得到位置指纹特征。最后用融合随机森林(Random Forest, RF)和多层感知机(Multilayer Perceptron, MLP)的新型集
成体系结构进行分类，获得目标位置样本的估计位置。
To address the problem of poor localization performance caused by the small number of fingerprint points and the lack of
representativeness of fingerprint features in the offline phase of fingerprint-based indoor localization, a CSI-based indoor localiza‐
tion method is proposed using Inverse Distance Weighted (IDW) interpolation and amplitude-phase fusion network. The IDW inter‐
polation algorithm is used to generate a large-capacity fingerprint library. Then, a parallel convolutional neural network (CNN) is
used to process the amplitude and phase to obtain the positional fingerprint features. Finally, a novel integrated architecture fusing
Random Forest (RF) and Multilayer Perceptron (MLP) is used for classification to obtain the estimated positions of the target loca‐
tion samples.
