(a) Blocked satellite; (b) multipath satellite; (c) NLOS satellite. red represents the NLOS signal. Ideally, the two signals should be completely separate, i.e., they should have distinct boundaries. However, they are now partially mixed together due to noise interference. We aim to distinguish between different signal types using our proposed method. In addition, current learning-based methods suffer from difficulties in constructing a large number of label databases, low model detection accuracy, and poor generalization. Furthermore, the above works discuss urban canyon environments only, so NLOS detection in urban forest environments is still an open problem. From a technical standpoint, we mainly address the following challenges:  
1. How to find representative features automatically with the help of unlabelled data. Current NLOS signal detection methods based on supervised learning have the problem of the difficulty of constructing a large number of labeled databases. 2. How to improve the generalization and robustness performance of NLOS detection models. Current unsupervised-learning-based NLOS signal-detection methods usually use a model of clustering, which cannot fully understand the complex relationship between the data, resulting in a low model detection accuracy. Furthermore, the different environmental noises around the receiver lead to a robustness that needs to be improved. To overcome the above difficulties, in this article, inspired by the success of deep learning (DL) techniques, we proposed a novel NLOS multipath detection method, i.e., the convolutional denoising autoencoder (CDAENet). Unsupervised feature learning can effectively solve the problem of a small number of labels. It trains the model by using a small number of labeled samples and a large number of unlabeled samples at the same time so that the model has sufficient learning ability in the case of a small number of labels. At the same time, we add noise to the data to reduce the model's dependence on inaccurate input data, which in turn increases the model's generalization performance and robustness. In detail, the flow chart of the CDAENet training algorithms is shown in Figure 3. First, we introduce deep DAE to capture the low-dimensional features of the BDS data, achieving dimensionality reduction while providing robust signal reconstruction. Speed is important for BDS real-time localization. As our input data are high-dimensional time series data, the computation of the model can be effectively reduced with dimensionality reduction. The reliability of the recognition results and the robustness of the model are improved by incorporating Gaussian noise. As far as we know, this is the first study using DAE for BDS recognition applications. Then, to further improve the detection accuracy of the model, we propose a multi-layer perceptron (MLP) model to capture the nonlinear correlation of NLOS. Finally, we verify the superiority of the proposed model in a real urban forest environment. Figure 2: Point cloud of RINEX-level features of BDS signals. The experimental results show that our method obtains more accurate detection results than previous methods. The main contributions of the proposed CDAENet for BDS NLOS signals detection are listed as follows:  
1. To improve the NLOS detection performance in urban forest environments, we develop an efficient CDAENet method that introduces \(C/N_{0}\), PR, and EA as input features to achieve the detection of NLOS signals with long time series. 2. To solve the difficulty of constructing a large number of labeled databases, we use the DAE module to learn low-dimensional representation in time series data, achieve efficient unsupervised feature extraction, and provide robust signal reconstruction. Meanwhile, the robustness of the model is improved by adding noise. 3.