针对提高Wi-Fi指纹室内定位技术性能，提出了一种基于卷积神经网络（Convolutional Neural Networks，CNN）的信道状态信息（Channel State Information，CSI）指纹室内定位方法。该方法利用CNN对定位环境参考点的幅度差和相位差信息联合作为离线阶段训练数据，保存CNN网络模型作为指纹；在线阶段，针对不同实验场景，对测试数据的幅度差信息和相位差信息进行加权处理，引入改进的基于概率的指纹匹配算法，将待定位点的CSI信息通过CNN网络模型估计出待定位点的坐标。此外，为增强算法普适性，针对复杂室内场景，提出了双节点定位方案来提高定位精度。在廊厅和实验室室内两种不同定位场景进行了实验，信息联合定位算法分别获得了28.4 cm和65.3 cm的平均定位误差，验证了所提算法的有效性。
Fingerprint indoor localization technology based on Wi-Fi has been widely used due to its good accuracy and easy implementation. Most of them use Received Signal Strength (RSS) as fingerprint because RSS is easy to obtain, but due to the poor stability of RSS, CSI has gradually become the mainstream of fingerprint due to its excellent stability. Traditional fingerprint location uses KNN or Bayes for fingerprint matching. On the one hand, it has high computational complexity, on the other hand, there is a large error in positioning accuracy. With the development of deep learning, combining fingerprint location with a neural network, using the excellent feature extraction ability of the neural network, we can extract higher dimensional signal features of Wi-Fi signal, and further improve the positioning accuracy. In order to improve the performance of Wi-Fi fingerprint indoor localization technology, a channel state information (CSI) fingerprint indoor localization method based on convolutional neural networks (CNN) is proposed. This method uses the amplitude difference and phase difference information of the reference point of the positioning environment as the offline training data, and saves the CNN network model as the fingerprint; In the online phase, according to different experimental scenarios, the amplitude difference information and phase difference information of the test data are weighted, and an improved probability-based fingerprint matching algorithm is introduced to estimates the coordinates of the test points by CNN network model. In addition, in order to enhance the universality of the algorithm, a two-node localization scheme is proposed to improve the positioning accuracy for complex indoor scenes. Experiments are carried out in two different positioning scenarios: the corridor and the laboratory. The average positioning error of the information joint localization algorithm is 28.4 cm and 65.3 cm respectively, which verifies the effectiveness of the proposed algorithm.