arXiv subjects

Junwoo Song

Publications and source records attributed to Junwoo Song.

3 recordsLinked to original sources

arXiv2025

Path-Based Correlation Analysis of Meteorological Factors and eLoran Signal Delay Variations

Unlike GNSS, which is vulnerable to jamming and spoofing due to its inherently weak received power, eLoran exhibits robustness owing to its high field strength. Therefore, the eLoran system can maintain reliable operation even in scenarios where GNSS becomes unavailable. However, since eLoran signals propagate through ground waves, the propagation delay is susceptible to changes in surface conditions, including both terrain and meteorological variations. This study aims to analyze the correlation between the temporal variations in eLoran signal propagation delay and meteorological factors at various points along the signal path.

eess.SP↗

arXiv2025

Correlation Analysis Between MF R-Mode Temporal ASF and Meteorological Factors

As the vulnerabilities of global navigation satellite systems (GNSS) have become more widely recognized, the need for complementary navigation systems has grown. Medium frequency ranging mode (MF R-Mode) has gained attention as an effective backup system during GNSS outages, owing to its strong signal strength and cost-effective scalability. However, to achieve accurate positioning, MF R-Mode requires correction for the additional secondary factor (ASF), a propagation delay affected by terrain. The temporal variation of ASF, known as temporal ASF, is typically corrected using reference stations; however, the effectiveness of this method decreases with distance from the reference station. In this study, we analyzed the correlation between temporal ASF and meteorological factors to evaluate the feasibility of predicting temporal ASF based on meteorological factors. Among these factors, temperature and humidity showed significant correlations with temporal ASF, suggesting their potential utility in ASF correction.

eess.SP↗

arXiv2019

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Reductions

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

math.OC↗