An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments  
[PERSON]  
1 School of Automation, Guangdong University of Technology, Guangzhou 510006, China; [EMAIL_ADDRESS] (Y:Q); [EMAIL_ADDRESS] (HZ). [PERSON]  
1 School of Automation, Guangdong University of Technology, Guangzhou 510006, China; [EMAIL_ADDRESS] (Y:Q); [EMAIL_ADDRESS] (HZ). [PERSON]  
311 Center for Intelligent Batch Manufacturing Based on IoT Technology (GDUT), Guangzhou 510006, China; [EMAIL_ADDRESS] 15 The Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT), Ministry of Education, Guangzhou 51000  
inaccurate positioning. These errors can be categorized into three main types related to satellites, signal propagation paths, and receiving equipment (i.e., transmitters, propagators, and receivers), for example, the error [5; 6; 7] generated when the signal is penetrating through the ionosphere and troposphere, the error caused by the Doppler effect of high-speed satellite movement, the error of the multipath effect, the error of the satellite clock, the error of ephemeris (satellite position and speed), the error of internal noise, etc. Most of the errors can be eliminated or weakened by using the short-baseline double difference (SBDD) technical [8]. SBDD positioning methods require the receiver to be very close to one or more reference stations, usually no more than a few tens of meters. Because the baseline is short, the effect of errors is small, and high-accuracy positioning can be achieved under relatively simple conditions. Meanwhile, satellite clock and receiver clock errors are eliminated, and ephemeris errors, ionospheric delays, tropospheric delays, and other spatially correlated errors are greatly weakened. However, NLOS cannot be eliminated and has become a major problem for inaccurate IDS positioning. Figure 1 shows satellite signal categories in an urban forest environment [9]. Specifically, the satellite signal in Figure 1a is completely obscured by trees and cannot be received by the receiver. In Figure 1b, a multipath signal occurs when the satellite signal is reflected by nearby trees, causing interference with the direct signal. Figure 1c shows the NLOS signal, where the receiver can only pick up signals reflected or scattered by nearby trees, which can produce positioning errors of hundreds of meters [10; 11]. Therefore, multipath interference in urban forest environments has become an important research topic in IDS urban positioning. Recently, artificial intelligence (AI) methods have gained a lot of attention, and most of them focus on supervised learning methods to recognize multipath and NLOS, e.g., k-nearest neighbor (KNN) [12], support vector machines (SVM) [13], decision tree (DT) [14], and convolutional neural network (CNN) [15] methods. These supervised learning methods are able to automatically acquire potential high-dimensional features for multipath and NLOS, achieving high recognition accuracy. However, it requires the construction of datasets containing a large number of labels, which is expensive and time-consuming. To alleviate the above problems, a multipath identification method based on unsupervised K-means [16; 17; 18] is proposed. However, this unsupervised machine learning (ML) method is less effective than supervised learning methods in terms of recognition accuracy. In harsh urban forest environments, which are affected by dense foliage, the BDS signal propagation direction is complex and variable, making the extracted features linearly indivisible [19]. We used an u-blox F9 receiver to receive the data in RINEX format; the exact way that the data were collected is described in detail in Section 5. Figure 2 shows a 3D point cloud of the carrier-to-noise ratio (\(C/N_{0}\)), the elevation angle (EA), and the pseudorange residual (PR) of the signal, where blue represents the LOS signal and  
Figure 1: Three satellite signal types of LOS, multipath, and NLOS in an urban forest environment.