基于无线局域网（wireless fidelity, WiFi）信道状态信息（channel state information, CSI）的室内定位方法有广泛应用需求，但是WiFi信号在噪声环境下有效特征会被遮挡，导致定位精度降低。针对这一问题，提出了基于深度残差收缩网络（deep residual shrinkage network, DRSN）的室内定位方法，该网络在非线性变换层中引入软阈值化，以消除不重要的特征，同时为了提高网络的学习能力，设置两个阈值学习通道，分别对指纹的每个天线和子载波学习幅度差阈值和相位阈值，并将其命名为逐天线与子载波的深度残差收缩网络（deep residual shrinkage network with antennas-wise and subcarriers-wise thresholds, DRSNAS）。离线阶段，针对不同的实验场景，对幅度差和相位信息进行加权联合，然后送入模型中训练。实验结果显示，定位精度在自习室场景可达1.54 m，在走廊场景可达0.99 m。

Indoor positioning methods based on CSI (channel state information) of WiFi (wireless fidelity) have extensive application demands. However, in noisy environments, the effective features of WiFi signals can be obscured, leading to a decrease in positioning accuracy. To address this issue, an indoor positioning method based on DRSN (deep residual shrinkage network) was proposed. This network introduces soft thresholding in the nonlinear transformation layer to eliminate unimportant features. To enhance the network's learning ability, two threshold learning channels were set up, which respectively learn the amplitude difference threshold and phase threshold for each antenna and subcarrier of the fingerprint, and it was named DRSN-AS (deep residual shrinkage network with antennas-wise and subcarriers-wise thresholds). In the offline stage, for different experimental scenarios, the amplitude difference and phase information were weighted and combined, and then sent to the model for training. Experimental results show that the positioning accuracy can reach 1.54 m in the self-study room scenario and 0.99 m in the corridor scenario.