Yue Yuan

The Global Navigation Satellite System (GNSS) has been widely adopted in navigation applications due to its high accuracy and convenience. However, in urban canyon environments, severe signal blockage caused by buildings and trees introduces substantial non-line-of-sight errors and multipath effects, leading to degraded and highly fluctuating positioning performance. To address this issue, this paper proposes a residual-guided hybrid stochastic modeling framework. The method operates in two stages: first, a pseudo-range correction estimation network takes GNSS parameters containing residuals as input to estimate pseudo-range correction; second, these estimated corrections together with elevation angle and carrier-to-noise ratio are fed into a hybrid stochastic model parameter estimation network to determine model parameters. This design adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios. Meanwhile, the explicit modeling of relationships among pseudo-range correction, elevation angle, and carrier-to-noise ratio renders the stochastic model parameters interpretable. Experiments on public urban GNSS datasets demonstrate that the proposed method achieves competitive positioning performance against both conventional and learning-based baselines. It delivers notable accuracy improvements in light urban canyon environments, particularly on the KLT2 sequence, while maintaining robust and competitive performance in the more challenging TST and Mong Kok scenarios. These results validate the effectiveness of jointly estimating pseudo-range corrections and adaptive observation weights for enhancing positioning accuracy and robustness across diverse urban environments.

Robust trajectory prediction of traffic participants is crucial for autonomous driving in complex urban environments. Traditional data-driven models predominantly rely on feature correlations and fail to explicitly model the causal relationships underlying traffic interactions. Motivated by this challenge, we propose a novel Causally-aware Hierarchical Graph Network, called CHGNet, for accurate and reliable vehicle trajectory prediction. The proposed framework enhances the robustness of autonomous vehicles by incorporating lane-aware scene context and jointly modeling both individual vehicle dynamics and inter-vehicle interactions. CHGNet introduces three key innovations: (i) a lane-aware spatio-temporal Transformer module that mitigates the impact of trajectory deviations and uncertainties from both temporal and spatial perspectives; (ii) a hierarchical graph neural network that effectively captures multi-level interaction patterns within the driving scene; and (iii) a causal reasoning module that explicitly models causal dependencies among vehicles while suppressing spurious correlations. Experimental results on the real-world dataset demonstrate that the proposed framework is resilient to imperfect tracklets and achieves competitive performance relative to state-of-the-art approaches, validating its effectiveness in improving predictive accuracy and robustness in complex traffic scenarios.

Accurately predicting the future motion of traffic participants is essential for autonomous driving in complex urban traffic scenes. Due to the frequent interactions and positioning uncertainties in the perception system, it is important to enable efficient spatial and temporal modeling of diverse entities and provide reliable distributions for future trajectories. In this work, we propose a novel Lane-aware Dynamic Spatio-temporal Transformer, called DSTformer, for effective and accurate vehicle trajectory prediction. The system aims to improve the robustness of autonomous vehicles by incorporating lane-aware scene constraints and utilizing both dynamic activities and scene context information. Specifically, we incorporate two masking methods to mitigate the impact of deviations and uncertainties from temporal and geographic spatial propagation views. Vehicles' trajectories that violate the expected lane-following rules are penalized via the Transformer-based modeling. Semantic neighborhood information is also employed via a graph masking method to capture the long-range spatial dependencies in traffic data. Additionally, the centerline waypoints based map structure is used to capture the vehicle-lane relations and enable multimodal trajectory prediction. Experimental results on the real-world dataset demonstrate that the proposed framework is resilient to imperfect data and achieves competitive performance with fewer parameters compared to current state-of-the-art prediction methods.

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

Step Frequency Continuous Wave Ground Penetrating Radar (SFCW-GPR), as a tool for nondestructive testing of shallow soil surface targets, the realization of the function of SFCW-GPR is mainly based on the theory of refraction, reflection and scattering of electromagnetic wave in the discontinuity of dielectric constant. So, the UWB antenna system, an important part of SFCW-GPR, becomes more indispensable. In this paper, an improved modified universal antenna is designed, simulated and fabricated. Based on a typical Bow-tie antenna, it is modified by the methods of lumped loads, cavity-backed loading and structure loading. The simulated and measured results show that the UWB antenna has 1.36 GHz bandwidth from 0.64 to 2.0 GHz with three resonant wavelength peaks, and having been modified and improved, the UWB antenna performances including voltage standing-wave ratio (VSWR), input impedance, the boresight gain and current distribution, are much better than the typical Bow-tie antenna. In addition, the results of verification experiment of Step Frequency Continuous Wave (SFCW) show that the antenna can be applied to the working scenarios of SFCW-GPR.

As one of the most popular Generation IV nuclear energy system, High Temperature Gas Cooled Reactor (HTGR) has outstanding inherent safety features. However, the nonlinearity and complexity of the modular-HTGR-based nuclear steam supplying system (NSSS) has put higher requirements to its control system. Based on the basic ideas and theories of fuzzy system, fuzzy control method provides a powerful tool to the control of nonlinear systems. This paper built the T-S fuzzy model of the modular-HTGR-based nuclear steam supplying system (NSSS), and designed the steam temperature T-S fuzzy controller using the parallel distributed compensation (PDC) method. Simulation shows that the designed T-S fuzzy controller has a better effect than the traditional PID control method.