Search Results (3)

Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move at high speeds, traditional pseudolite algorithms struggle to establish accurate physical models linking signals to spatial domains. This study introduces a deep learning-based pseudolite positioning algorithm leveraging a spatio-temporal fusion framework to address challenges such as signal attenuation, multipath effects, and non-line-of-sight (NLOS) effects. The Enconv1d model we developed is based on the spatio-temporal characteristics of the pseudolite observation signals. The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy. Full article

Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity. Full article

In urban canyon environments, Global Navigation Satellite System (GNSS) satellites are heavily obstructed with frequent rise and fall and severe multi-path errors induced by signal reflection, making it difficult to acquire precise, continuous, and reliable positioning information. To meet imperative demands for high-precision positioning of public users in complex environments, like urban canyons, and to solve the problems for GNSS/pseudolite positioning under these circumstances, the Global Navigation Satellite System (GNSS) Precision Point Positioning (PPP) algorithm combined with a pseudolite (PLS) was introduced. The former problems with the pseudolite PPP technique with distributed pseudo-satellites, which relies heavily on known points for initiation and prerequisite for previous high-precision time synchronization, were solved by means of a real-time equivalent clock error estimation algorithm, ambiguity fixing, and validation method. Experiments based on a low-cost receiver were performed, and the results show that in a weak obstructed environment with low-density building where the number of GNSS satellites was greater than seven, the accuracy of pseudolite/GNSS PPP with fixed ambiguity was better than 0.15 m; when there were less than four GNSS satellites in severely obstructed circumstances, it was impossible to obtain position by GNSS alone, but with the support of a pseudolite, the accuracy of PPP was able to be better than 0.3 m. Even without GNSS, the accuracy of PPP could be better than 0.5 m with only four pseudolites. The pseudolite/GNSS PPP algorithm presented in this paper can effectively improve availability with less GNSS or even without GNSS in constrained environments, like urban canyons in cities. Full article