arXiv subjects

Seunghyeon Park

Publications and source records attributed to Seunghyeon Park.

5 recordsLinked to original sources

arXiv2026

Lunar Surface Receiver Clock Synchronization Using GNSS and Satellite Nadir Angle

The use of signals from Earth-orbiting Global Navigation Satellite System (GNSS) constellations for positioning, navigation, and timing on the lunar surface was recently demonstrated by the Lunar GNSS Receiver Experiment (LuGRE) aboard Blue Ghost Mission 1. For receiver clock synchronization using GNSS, Kalman filter performance depends on the satellite-specific measurement uncertainty used to construct the measurement noise covariance. Terrestrial measurement weighting commonly relies on receiver elevation angle, but the elevation angles in the analyzed LuGRE data are confined to 24.2-31.5 degrees, providing limited discrimination of measurement quality. In contrast, satellite nadir angles span 12.1-66.6 degrees, and the measurement variability increases substantially at small nadir angles. Based on this observation, we propose a measurement weighting model that combines satellite nadir angle with C/N0 for lunar surface receiver clock synchronization. The proposed model is evaluated against three weighting methods adopted from the literature using seven LuGRE operation periods (OPs). It achieves the lowest receiver clock bias root mean square error (RMSE) in six of the seven OPs and an overall RMSE of 5.78 m, which is 19.5% lower than that of the best comparison model. The proposed model also provides the lowest clock bias prediction RMSE under simulated measurement outages.

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arXiv2026

LEO Doppler Matching from Power Spectrum Data with Continuity-Based Segmentation and Multi-Position Clock Offset Estimation

Low Earth orbit (LEO) satellite Doppler measurements extracted from passive software-defined radio (SDR) spectrum data require temporal alignment with predicted satellite trajectories for reliable satellite association. In our previous framework, Doppler slope information was used during both segment generation and clock offset estimation, while the clock offset was estimated at a single coarse receiver position. This study proposes a satellite matching framework based on continuity-based Doppler segmentation and multi-position clock offset estimation to reduce these dependencies. Doppler segments are first generated using only temporal and frequency continuity, thereby separating segment generation from the slope criterion used for clock offset estimation. The clock offset is then estimated using five coarse receiver positions, consisting of one city-level nominal position and four surrounding positions, through a two-stage procedure based on slope compatibility and a combined Doppler cost. The aligned segments are subsequently associated with candidate satellites using a matching score based on the same Doppler slope and bias-removed RMSE metrics, and the resulting associations are evaluated through receiver localization. Experiments using six hours of passive Starlink/OneWeb monitoring data show that continuity-based segmentation alone did not improve receiver localization compared with slope-based segmentation under single-position clock offset estimation. However, applying the proposed multi-position clock offset estimation to the same continuity-based Doppler segments reduced the localization error from 32.99 km to 1.84 km, achieving the lowest error among the evaluated methods. The results for the evaluated dataset show improved spatial consistency of satellite associations when multi-position clock offset estimation is applied to continuity-based Doppler segments.

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arXiv2025

Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data

The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. However, the primary sources of error for GNSS and eLoran differ. For eLoran, the main source of error is signal propagation delay over land, known as the additional secondary factor (ASF). This delay, influenced by ground conductivity and weather conditions along the signal path, is challenging to predict and mitigate. In this paper, we measure the time difference (TD) between GPS and eLoran using a time interval counter and analyze the correlations between eLoran/GPS TD and eleven meteorological factors. Accurate estimation of eLoran/GPS TD could enable eLoran to achieve timing accuracy comparable to that of GPS. We propose two estimation models for eLoran/GPS TD and compare their performance with existing TD estimation methods. The proposed WLR-AGRNN model captures the linear relationships between meteorological factors and eLoran/GPS TD using weighted linear regression (WLR) and models nonlinear relationships between outputs from expert networks through an anisotropic general regression neural network (AGRNN). The model incorporates terrain elevation to appropriately weight meteorological data, as elevation influences signal propagation delay. Experimental results based on four months of data demonstrate that the WLR-AGRNN model outperforms other models, highlighting its effectiveness in improving eLoran/GPS TD estimation accuracy.

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arXiv2025

CSAC Drift Modeling Considering GPS Signal Quality in the Case of GPS Signal Unavailability

The Global Positioning System (GPS), one of the Global Navigation Satellite Systems (GNSS), provides accurate position, navigation and time (PNT) information to various applications. One of the application that is highly receiving attention is satellite vehicles, especially Low Earth Orbit (LEO) satellites. Due to their limited ways to get PNT information and low performance of their onboard clocks, GPS system time (GPST) provided by GPS is a good reference clock to synchronize. However, GPS is well-known for its vulnerability to intentional or unintentional interference. This study aims to maintain the onboard clock with less error relative to the GPST even when the GPS signal is disrupted. In this study, we analyzed two major factors that affects the quality of the GPS measurements: the number of the visible satellites and the geometry of the satellites. Then, we proposed a weighted model for a Chip-Scale Atomic Clock (CSAC) that mitigates the clock error relative to the GPST while considering the two factors. Based on this model, a stand-alone CSAC could maintain its error less than 4 microseconds, even in a situation where no GPS signals are received for 12 hours.

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arXiv2023

Detection of Pedestrian Turning Motions to Enhance Indoor Map Matching Performance

A pedestrian navigation system (PNS) in indoor environments, where global navigation satellite system (GNSS) signal access is difficult, is necessary, particularly for search and rescue (SAR) operations in large buildings. This paper focuses on studying pedestrian walking behaviors to enhance the performance of indoor pedestrian dead reckoning (PDR) and map matching techniques. Specifically, our research aims to detect pedestrian turning motions using smartphone inertial measurement unit (IMU) information in a given PDR trajectory. To improve existing methods, including the threshold-based turn detection method, hidden Markov model (HMM)-based turn detection method, and pruned exact linear time (PELT) algorithm-based turn detection method, we propose enhanced algorithms that better detect pedestrian turning motions. During field tests, using the threshold-based method, we observed a missed detection rate of 20.35% and a false alarm rate of 7.65%. The PELT-based method achieved a significant improvement with a missed detection rate of 8.93% and a false alarm rate of 6.97%. However, the best results were obtained using the HMM-based method, which demonstrated a missed detection rate of 5.14% and a false alarm rate of 2.00%. In summary, our research contributes to the development of a more accurate and reliable pedestrian navigation system by leveraging smartphone IMU data and advanced algorithms for turn detection in indoor environments.

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