arXiv subjects

Jiwon Seo

Publications and source records attributed to Jiwon Seo.

At least 19 recordsLinked to original sources

arXiv2026

Clock-Shift-Aided Blind Doppler Matching in Passive SDR Monitoring Data for LEO Doppler Positioning

Low Earth orbit (LEO) satellite signals have recently attracted increasing attention as opportunistic signals for positioning, navigation, and timing. Most Doppler-based LEO positioning studies rely on tracked satellite signals or raw in-phase/quadrature (IQ) samples that preserve signal-level structures for acquisition, Doppler tracking, or satellite association. In contrast, passive software-defined radio (SDR) monitoring systems often retain only time-frequency power scans, in which LEO Doppler traces appear as unlabeled ridges without raw IQ samples or identifiable signal structures. This paper proposes a clock-shift-aided segment-based blind Doppler matching method that associates unlabeled Doppler traces with candidate satellites predicted using two-line element (TLE) data. The proposed framework consists of slope-consistent Doppler segment generation, clock-shift-aided temporal alignment, and multi-metric satellite matching based on Doppler-rate and trajectory-shape consistency. The proposed method was evaluated using a three-hour subset of open Starlink/OneWeb monitoring data collected in the United Kingdom. The clock-offset search selected an effective offset of +19 s, for which 56 of 69 segments had at least one slope-compatible candidate, compared with 19 segments at zero shift. Using the resulting satellite associations, the batch positioning error over the full three-hour period was 104.2 m, whereas the RMSE-based and slope-based simple-segment baselines failed to converge near the reference receiver location.

eess.SP↗

arXiv2026

Robust 3DMA GNSS Positioning in Urban Areas Using Conservative Candidate Set Determination Based on GNSS Shadow and Reflection

In urban environments, global navigation satellite system (GNSS) positioning is often degraded by signal blockage and reflection caused by surrounding buildings. To address this problem, various three-dimensional mapping-aided (3DMA) GNSS techniques have been proposed. Among them, zonotope shadow and reflection matching (ZSRM) has demonstrated effective positioning performance by determining the candidate receiver position set using predicted GNSS shadow and reflection regions. However, ZSRM remains vulnerable to satellite signal misclassification, which may lead to positioning failure. To address this limitation, this paper proposes a conservative candidate set determination strategy for robust ZSRM positioning. Instead of using only the most probable signal reception condition from an artificial intelligence (AI)-based classifier, the proposed method combines the candidate regions associated with the two most probable classes. This allows the candidate receiver position set to be determined more conservatively, reducing the risk of positioning failure under signal misclassification. The proposed method was evaluated using real-world urban GNSS data. Experimental results show that the proposed method improved the positioning success rate to 100% for RF, GBDT, and SVM classifiers, compared with 8.2%-30.1% for the existing ZSRM algorithm using individual classifier outputs and 96.6% for ZSRM with unanimous voting. Although the proposed method increased the position bounds by retaining additional candidate regions, it produced smaller position bounds than ZSRM with unanimous voting. These results demonstrate that the proposed method substantially enhances the robustness of ZSRM under satellite signal misclassification in urban GNSS environments.

eess.SP↗

arXiv2026

From Tunnel Overburden Reconstruction to Tidal Measurements via atmospheric muon

Muography with scintillator-based detectors enables the investigation of density distributions within large structures. The Hankuk Atmospheric-muon Wide Landscaping (HAWL) project is the first real-time portable muon tomography. It successfully charted the mountainous region of eastern Korea by measuring cosmic-ray muons with a detector operated in a moving vehicle. HAWL achieved a tunnel length accuracy of 6.0 % and an overburden measurement range of 8-400 meter-water-equivalent (m.w.e.). The HAWL upgrade (HAWLU) features a more compact design and enables trajectory reconstruction. Using this upgraded system, we measured tidal water-level variations with meter-scale precision over 31.6 days in the Boryeong Undersea Tunnel. The measured muon rate decreased by $1.72 \pm 0.04$ % per meter of increasing tidal level, while the reconstruction of undersea tunnel scan independently validated the detector performance. These results demonstrate the capabilities of dynamic muography in two approaches, one with a portable detector in motion and the other with a detector measuring a target that changes over time.

physics.ins-det↗

arXiv2026

Precision measurement of ocean tides using cosmic-ray muons

Cosmic-ray muons provide a natural probe of the integrated mass along their trajectories. Here we show that cosmic-ray muons can monitor ocean-tide-induced variations in seawater overburden within an underwater tunnel. The observed modulation closely tracks independent tide-gauge measurements, yielding an attenuation response coefficient of $k=43.2\pm1.0$~counts$\rm /h/m$ and resolving meter-scale variations in seawater overburden. A spatial muography scan validates the overburden model, confirming that the seawater contribution is essential to reproduce the observed attenuation. Unlike conventional tide gauges that measure sea level locally, muography measures the integrated overburden above the detector. These results demonstrate that muography can quantitatively resolve small, time-dependent variations in environmental overburden, providing a new approach to monitoring dynamic natural systems.

hep-ex↗

arXiv2026

Spexis: Speculative Lookahead Scheduling for LLM Inference

Spexis is a multi-GPU LLM inference framework that improves the efficiency of pipeline and tensor parallelism through speculative parallelism. Rather than using speculative decoding only to accelerate token generation, Spexis runs speculation in parallel with normal execution, introducing a new parallelism axis without increasing KV-cache memory usage. This improves memory efficiency and helps mitigate the bottlenecks of multi-GPU inference. Spexis further uses lookahead scheduling to predict speculation quality and future memory pressure, allowing it to reduce wasted speculation, KV-cache eviction, and recomputation. Built on top of vLLM, Spexis largely improves serving performance across a range of GPU configurations, achieving speedups of up to 34% over a baseline that uses the optimal combination of pipeline and tensor parallelism. Spexis's source code is publicly available at https://github.com/mlsys-seo/spexis.

cs.LG↗

arXiv2026

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate

Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces memory footprint without retraining by leveraging a small calibration set. Recent Hessian-based PTQ methods compensate quantization error via cross-channel dependencies, but such approaches degrade at low bit-widths due to noisy curvature estimates from limited calibration data. We propose DASH-Q, a robust PTQ framework using diagonal Hessian approximation and iterative weighted least squares. By discarding noise-prone dependencies, DASH-Q filters sampling noise while prioritizing the preservation of salient feature power. We outperform other PTQ baselines in ultra low-bit regime, improving zero-shot accuracy by 7.01% on average and up to 14.01% over the strongest baselines across five baseline LLM models, while showing robust and stable performance with very small calibration data.

cs.LG↗

arXiv2026

Quantum-Based k-Coverage Optimization for UAV-Aided Search and Rescue Missions

In large-scale disaster scenarios, rapid localization of missing persons is a critical challenge for search-and-rescue (SAR) operations. Unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) receivers can support RF-based localization by collecting signals emitted from mobile devices at spatially distributed sensing locations. This paper addresses the resulting waypoint-selection problem: determining a minimum set of UAV waypoints that provides at least threefold coverage of every potential target location. We formulate this task as an extended k-coverage problem that independently defines the UAV-navigable and target regions, and derive an exact-penalty quadratic unconstrained binary optimization (QUBO) formulation with a sufficient penalty condition that preserves feasibility and minimum waypoint cardinality. The QUBO is mapped to an Ising-form cost Hamiltonian and evaluated using the quantum approximate optimization algorithm (QAOA) on both a noise-free simulator and IBM's 127-qubit Eagle processor. On the tested simulator instances, QAOA recovers the known minimum-cardinality solutions. Across the rectangular hardware test cases, the mean 3-coverage ratio exceeded 95%. In the campus-scale evaluation, ten hardware executions achieved 99.3% mean 3-coverage with a 90% feasible-run rate, while the shortest feasible flight path was up to 37.0% shorter than those of the deterministic grid-based baselines. Additional comparisons with classical optimization and learning-based baselines are provided, together with computational and quantum-resource analyses for larger generated instances. These results establish an exact QUBO representation for RF-based SAR waypoint selection and characterize its implementation on current gate-based quantum hardware.

eess.SY↗

arXiv2026

Rigidity-Based Multi-UAV Trajectory Optimization for Rapid Cooperative Emergency Target Localization

Reducing the response time for accurate emergency-caller localization is critical in vehicular and public-safety networks. Although mobile devices commonly use GNSS, Wi-Fi, or cellular positioning, their accuracy and availability can degrade because of poor signal reception, limited infrastructure, and regulatory constraints. UAV-based localization offers a promising alternative by using airborne sensors to cooperatively estimate the target position. However, existing Fisher information matrix (FIM)-based trajectory optimization methods depend on the current target estimate and can perform poorly in the early mission stage, when measurements are limited and uncertainty is high. We propose a rigidity-based UAV trajectory optimization method that maximizes the smallest nonzero singular value of the rigidity matrix associated with the UAV-target sensing graph, improving geometric conditioning and reducing position ambiguity. We also introduce a pruning-based matrix reduction strategy for efficient real-time implementation. Simulations show that the proposed method reduces search time by 32.9% compared with FIM-based methods and satisfies the FCC horizontal emergency-localization requirement sooner. Further results demonstrate scalability, robustness to UAV positioning errors and NLOS path loss, low sensitivity to heading parameters, practical computation and communication costs, and more stable degradation than PPO-based baselines under severe sensing and navigation perturbations.

eess.SY↗

arXiv2026

Asymmetric Collapse in Model Merging: When Refusal Over- writes Recognition

Model merging is often used to combine capabilities from separately fine-tuned models without additional training, but it is unclear whether standard merging methods preserve multiple safety-relevant behaviors simultaneously. We study this question through a controlled case study using two Gemma-3-1B-IT finetunes on two complementary safety objectives: CARES harm-level classification and WildJailbreak adversarial refusal. We merge the two fine-tunes using Linear, SLERP, TIES, and DARE-TIES, and evaluate the merged models on classification accuracy, attack resistance, and benign compliance. Across all four methods, attack resistance transfers significantly more than classification accuracy: merged models retain 81-85% jailbreak refusal rates while CARES accuracy falls to at most 12.9%. Weight-space measurements suggest that this asymmetry is not caused by strongly opposing task-vector directions: the two task vectors are nearly orthogonal (cosine similarity 0.011). Instead, the refusal fine-tune induces consistently larger per-layer task-vector magnitudes, causing magnitude-sensitive methods to favor refusal updates. These results show that standard model merging can collapse safety recognition into broad refusal when safety-relevant task vectors differ substantially in scale.

cs.AI↗

arXiv2026

Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding

LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to speculatively generate multiple tokens, which are then verified in parallel by a target model. However, when speculation accuracy is low, the overhead from rejected tokens can offset the benefits, limiting SD's effectiveness, especially at large batch sizes. To address this, we propose Speculative Verification (SV), an efficient augmentation to SD that dynamically predicts speculation accuracy and adapts the verification length to maximize throughput. SV introduces a companion model - a small auxiliary model similar in size to the draft model - to estimate the alignment between draft and target model distributions. By maximizing the information gain from quantifying this alignment, SV refines verification decisions, reducing wasted computation on rejected tokens and improving decoding efficiency. Moreover, SV requires no modifications to the draft or target models and is compatible with existing SD variants. We extensively evaluated SV on publicly available LLMs across three NLP tasks using nine combinations of draft, companion, and target models, including 13B-72B target models and three types of variations: base (no finetuning), instruction-tuned, and task fine-tuned. Across all experiments and batch sizes (4-80), SV consistently outperforms both SD and standard decoding with the target model. It improves SD performance by up to 2$\times$, with an average speedup of 1.4 $\times$ in large-batch settings (batch sizes 32-80). These results demonstrate SV's robustness, scalability, and practical utility for efficient LLM inference.

cs.CL↗

arXiv2026

Zonotope Shadow and Reflection Matching: A Novel GNSS Reflection-Based Framework for Enhanced Positioning Accuracy in Urban Areas

In urban areas, signal reception conditions are often poor due to reflections from buildings, resulting in inaccurate global navigation satellite system (GNSS)-based positioning. Various 3D-mapping-aided (3DMA) GNSS techniques, including shadow matching, have been proposed to address this issue. However, conventional shadow matching estimates positions in a discretized manner. The accuracy of this approach is limited by the resolution of the grid points representing the candidate receiver positions, making it difficult to achieve robust urban positioning and to ensure that the position estimate satisfies user-specified protection levels or safety bounds. To overcome these limitations, zonotope shadow matching (ZSM) has been proposed, which utilizes a set-based position estimate rather than grid-based estimates. ZSM calculates the GNSS shadow--an area on the ground where the line-of-sight (LOS) is blocked and only non-line-of-sight (NLOS) signals can be received--to estimate the receiver's position set. ZSM distinguishes between LOS and NLOS satellites, determining that the receiver is inside the GNSS shadow if the satellite is NLOS and outside if the satellite is LOS. However, relying solely on GNSS shadows limits the ability to sufficiently reduce the size of the receiver position set and to precisely estimate the receiver's location. To address this, we propose zonotope shadow and reflection matching (ZSRM) to enhance positioning accuracy in urban areas. The proposed ZSRM technique is validated through field tests using GNSS signals collected in an urban environment. Consequently, the RMS horizontal position error of ZSRM improved by 10.0% to 53.6% compared with ZSM, while the RMS cross-street and along-street position bounds improved by 18.0% to 50.1% and 30.7% to 59.3%, respectively.

eess.SP↗

arXiv2025

FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks

Neural networks commonly execute on hardware accelerators such as NPUs and GPUs for their size and computation overhead. These accelerators are costly and it is hard to scale their resources to handle real-time workload fluctuations. We present FlexiQ, an adaptive mixed-precision quantization scheme for computer vision models. FlexiQ selectively applies low-bitwidth computation to feature channels with small value ranges and employs an efficient bit-lowering method to minimize quantization errors while maintaining inference accuracy. Furthermore, FlexiQ adjusts its low-bitwidth channel ratio in real time, enabling quantized models to effectively manage fluctuating inference workload. We implemented FlexiQ prototype, including the mixed-precision inference runtime on our custom NPU and GPUs. Evaluated on eleven convolution- and transformer-based vision models, FlexiQ achieves on average 6.6% higher accuracy for 4-bit models with finetuning and outperforms four state-of-the-art quantization techniques. Moreover, our mixed-precision models achieved an efficient accuracy-latency trade-off, with the 50% 4-bit model incurring only 0.6% accuracy loss while achieving 40% of the speedup of the 100% 4-bit model over 8-bit model. Latency evaluations on our NPU and GPUs confirmed that FlexiQ introduces minimal runtime overhead, demonstrating its hardware efficiency and overall performance benefits.

cs.LG↗

arXiv2025

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers

In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM) - were trained to perform line-of-sight (LOS) and non-line-of-sight (NLOS) classification based on global positioning system (GPS) signal features. A satellite is selected for positioning only when all classifiers unanimously agree on its classification and their associated confidence scores exceed a threshold. Experiments with real-world GPS data collected in dense urban areas demonstrate that the proposed method significantly improves the positioning success rate and the receiver containment rate, even with imperfect LOS/NLOS classification. Although a slight increase in the position bound was observed due to the reduced number of satellites used, overall positioning reliability was substantially enhanced, indicating the effectiveness of the proposed approach in urban GNSS environments.

eess.SP↗

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.

eess.SP↗

arXiv2025

Toward High Accuracy DME for Alternative Aircraft Positioning: SFOL Pulse Transmission in High-Power DME

The Stretched-FrOnt-Leg (SFOL) pulse is an advanced distance measuring equipment (DME) pulse that offers superior ranging accuracy compared to conventional Gaussian pulses. Successful SFOL pulse transmission has been recently demonstrated from a commercial Gaussian pulse-based DME in low-power mode utilizing digital predistortion (DPD) techniques for power amplifiers. These adjustments were achieved through software modifications, enabling SFOL integration without replacing existing DME infrastructure. However, the SFOL pulse is designed to optimize ranging capabilities by leveraging the effective radiated power (ERP) and pulse shape parameters permitted within DME specifications. Consequently, it operates with narrow margins against these specifications, potentially leading to non-compliance when transmitted in high-power mode. This paper introduces strategies to enable a Gaussian pulse-based DME to transmit the SFOL pulse while adhering to DME specifications in high-power mode. The proposed strategies involve use of a variant of the SFOL pulse and DPD techniques utilizing truncated singular value decomposition, tailored for high-power DME operations. Test results, conducted on a testbed utilizing a commercial Gaussian pulse-based DME, demonstrate the effectiveness of these strategies, ensuring compliance with DME specifications in high-power mode with minimal performance loss. This study enables cost-effective integration of high-accuracy SFOL pulses into existing high-power DME systems, enhancing aircraft positioning precision while ensuring compliance with industry standards.

eess.SP↗

arXiv2025

Set-Based Position Ambiguity Reduction Method for Zonotope Shadow Matching in Urban Areas Using Estimated Multipath Errors

In urban areas, the quality of global navigation satellite system (GNSS) signals deteriorates, leading to reduced positioning accuracy. To address this issue, 3D-mapping-aided (3DMA) techniques, such as shadow matching and zonotope shadow matching (ZSM), have been proposed. However, these methods can introduce a problem known as multi-modal position ambiguity, making it challenging to select the exact mode in which the receiver is located. Accurately selecting the correct mode is essential for improving positioning accuracy. A previous study proposed a method that uses satellite-pseudorange consistency (SPC), calculated from pseudorange measurements, to select the mode containing the receiver. This method achieved a mode selection accuracy of approximately 78%. To further enhance accuracy, the study utilized pseudorange measurements collected at multiple timesteps from a fixed location and a trained line-of-sight (LOS) classifier. However, in practice, collecting data at multiple timesteps from the same location in dynamic environments is challenging. Moreover, the performance of the trained LOS classifier heavily depends on the surrounding environment, leading to low reliability. In this study, we propose a method that estimates and corrects multipath errors based on the mode distribution obtained from the output of ZSM and extract an enhanced SPC using the corrected pseudorange measurements. This enables high mode selection accuracy using only single-timestep pseudorange measurements, without requiring a trained LOS classifier.

eess.SP↗

arXiv2025

Reducing Computational Complexity of Rigidity-Based UAV Trajectory Optimization for Real-Time Cooperative Target Localization

Accurate and swift localization of the target is crucial in emergencies. However, accurate position data of a target mobile device, typically obtained from global navigation satellite systems (GNSS), cellular networks, or WiFi, may not always be accessible to first responders. For instance, 1) accuracy and availability can be limited in challenging signal reception environments, and 2) in regions where emergency location services are not mandatory, certain mobile devices may not transmit their location during emergencies. As an alternative localization method, a network of unmanned aerial vehicles (UAVs) can be employed to passively locate targets by collecting radio frequency (RF) signal measurements, such as received signal strength (RSS). In these situations, UAV trajectories play a critical role in localization performance, influencing both accuracy and search time. Previous studies optimized UAV trajectories using the determinant of the Fisher information matrix (FIM), but its performance declines under unfavorable geometric conditions, such as when UAVs start from a single base, leading to position ambiguity. To address this, our prior work introduced a rigidity-based approach, which improved the search time compared to FIM-based methods in our simulation case. However, the high computational cost of rigidity-based optimization, primarily due to singular value decomposition (SVD), limits its practicality. In this paper, we applied techniques to reduce computational complexity, including randomized SVD, smooth SVD, and vertex pruning.

eess.SY↗

arXiv2024

Quantum Support Vector Machine-Based Classification of GPS Signal Reception Conditions

Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM.

eess.SP↗