## 2 Related Work  
Due to the complex urban forest environment and uncertain noise background, separating the two satellite signals, LOS and NLOS, is still a challenge. In recent years, various algorithms have been proposed to resolve this dilemma, which can mainly be classified into  
Figure 3: The flow chart of the proposed CDAENet training algorithms. three categories: (1) multi-sensor fusion methods, (2) machine-learning-based methods, and (3) deep-learning-based methods. Multi-sensor fusion methods. Methods in the first category use a comprehensive solution including the global navigation satellite system (GNSS) and multi-sensor integration. Several methods have been extensively explored for positioning in multipath and NLOS environments using sensors such as light laser detection and ranging (LiDAR), sky-pointing cameras, and inertial measurement units (IMU) in combination with GNSS. In addition, recent studies [20; 21; 22; 23] have utilized dynamic users at multiple locations on the road to generate multipath maps and employed residual-based algorithms to address location inaccuracies in urban canyon environments. Nagivation applications integrating a cubature Kalman filter (CKF) method and strap-down inertial navigation system (SINS) have been proposed to improve the navigation error under harsh GNSS environments [24]. However, this approach usually requires additional hardware devices, which not only increase the size of the BDS application but also increase the cost. As a result, the applicability of BDS positioning is severely limited and is not suitable for low-cost navigation services. The sensors could work well under different environmental conditions, such as volatile organic compounds monitoring and indoor gas monitoring [25]. Meanwhile, in outdoor environments, some sensors are more sensitive to light and weather, making them unsuitable for positioning in many complex scenarios. Among them, [26] found that raindrops have negative effects on LiDAR performance. In very clean sky conditions, e.g., after rain brings aerosols to the ground, the returning signal will be too weak for LiDAR to provide meaningful estimates when aerosol particles are absent. Ref. [27] pointed out that short-duration oscillations can be clearly observed by our sensor due to the influence of wind. Machine-learning-based methods. Methods in the second category consider learning-based approaches to detect multipath and NLOS. GNSS measurement errors in complex environments are difficult to fully model due to the influences of multipath and NLOS. Learning-based methods are trained using only data, avoiding strict assumptions about the structure of the data. Several studies have proposed supervised ML methods. Scholars have proposed the detection of \(C/N_{0}\), EA, AA, PR, and pseudo-distance residual consistency (PRC) using SVM [13], extreme gradient boosting (XGBoost) [28], random forest (RF) [29], DT [14], and KNN [12] methods, respectively. The above ML methods have their own advantages and limitations. Ref. [30] proposed a two-stage stacked integrated learning (SEL) method that combines the three ML methods. The first level uses SVM and XGBoost, and the second level uses logistic regression (LR) to detect NLOS. A series of works [16; 17; 18] used an unsupervised method of K-means clustering to detect multipath/NLOS. However, ML-based methods have poor generalization performance in different environments, resulting in the detection accuracy of current ML methods still needing to be improved. Deep-learning-based methods. Methods in the third category suggest that the DL can effectively solve nonlinear problems andq43 is widely used for the identification of BDS signals. For example, ref. [15] proposed a CNN multipath-signal-mitigation method. Experimental results show that the method achieves relatively good localization results in both dynamic and static environments. In [31], a DL model is proposed for the first time to detect GNSS image multipath signals in the time-frequency domain.