arXiv subjects

Li-Ta Hsu

Publications and source records attributed to Li-Ta Hsu.

At least 19 recordsLinked to original sources

arXiv2026

RadioRange: An Open-Source Digital Twin-based Ranging Simulator for UWB, Wi-Fi, and 5G

Accurate RF-based ranging is critical for location-aware wireless systems, yet no open platform exists for fair, reproducible comparison across protocols under realistic hardware impairments. Existing simulators target communication-layer metrics and lack ranging algorithms, impairment models, and positioning-specific evaluation. We present RadioRange, an open-source, positioning-first digital twin that unifies UWB, Wi-Fi, and 5G NR on identical ray-traced physical channels. The platform models eleven independently toggleable hardware impairments across three injection stages, spanning antenna-level offsets, RF circuit non-idealities, and post-compensation CSI residuals, each with documented physical models and protocol-specific defaults. Five first-path ranging detectors and three multipath identification algorithms are provided within a protocol-specific evaluation framework, enabling controlled Monte Carlo benchmarking and systematic ablation studies. The simulator is validated against real-world UWB and Wi-Fi measurements, demonstrating that the channel model captures geometry-dependent multipath bias. RadioRange-Sim is publicly available at https://github.com/Togure/RadioRange.

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arXiv2026

A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization

Decentralized multi-robot LiDAR-SLAM is essential for collaborative missions but faces significant challenges in maintaining global consistency. Existing frameworks predominantly rely on local-search optimization or one-time coordinate alignment, which are prone to suboptimal convergence and long-term inconsistency, especially in large-scale or degenerate environments. To address these limitations, this paper presents the first decentralized LiDAR-SLAM system that integrates a state-of-the-art certifiably optimal Pose Graph Optimization (PGO) backend. By leveraging the Riemannian Block Coordinate Descent (RBCD) algorithm, our system ensures globally consistent trajectory estimation without requiring accurate initial guesses. Experimental results demonstrate that the proposed framework achieves superior robustness, improving trajectory RMSE by up to 48.9% compared to the state-of-the-art DiSCo-SLAM.

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arXiv2026

CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is often unreliable in urban canyons. Existing differentiable factor graph optimization (DFGO) methods learn measurement weighting through the solver, but they still use position-only objectives. As a result, the position estimate may improve while the reported covariance remains too small, too large, or incorrectly oriented. We propose CredibleDFGO (CDFGO), a differentiable GNSS factor graph framework that makes covariance credibility an explicit training target. A Weighting Generation Network (WGN) predicts per-satellite reliability weights, and a differentiable Gauss-Newton solver maps these weights to a position estimate and a Hessian-derived posterior covariance. We use proper scoring rules to supervise the East-North predictive distribution end to end. We study negative log-likelihood (NLL), the energy score (ES), and their combination. Results on three UrbanNav test scenes show consistent gains in covariance credibility. Positioning accuracy also improves on the medium-urban and harsh-urban scenes; on the deep-urban scene, both the mean horizontal error and the 95th-percentile error improve. On the harsh-urban Mong Kok (MK) scene, CDFGO-Combined reduces the mean horizontal error from 13.77 m to 11.68 m, reduces NLL from 40.63 to 6.59, and reduces ES from 12.31 to 9.05 relative to DFGO (MAE). Case studies link the MK improvement to better axis-wise consistency, more credible local covariance ellipses, and satellite-level reweighting.

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arXiv2026

MIMO OFDM-Enabled ISAC for Low-Altitude Non-Cooperative UAV Surveillance: A Survey

The widespread use of unmanned aerial vehicles (UAVs) in low-altitude airspace has raised significant safety and security concerns, motivating the development of reliable non-cooperative UAV surveillance technologies. Integrated sensing and communication (ISAC), enabled by multiple-input multiple-output (MIMO) architectures and orthogonal frequency-division multiplexing (OFDM) waveforms, has emerged as a promising paradigm for leveraging cellular infrastructure to support large-scale sensing without additional hardware deployment. This paper presents the first comprehensive survey dedicated to MIMO OFDM-enabled ISAC for low-altitude non-cooperative UAV surveillance, where the targeted UAVs do not intentionally assist the monitoring system through dedicated signaling or prior coordinate sharing. We first analyze the unique propagation characteristics of low-altitude UAV sensing, including severe clutter, rapid channel variations, and mixed near/far-field effects, and discuss corresponding waveform design principles. We then systematically review existing MIMO OFDM-enabled UAV surveillance techniques along four key dimensions: ISAC system modeling and network optimization, UAV detection and tracking algorithms under single and networked base station (BS) architectures, UAV identification techniques based on micro-Doppler and learning-based approaches, and experimental validations and practical field trials. Subsequently, we summarize open challenges such as sensing under severe clutter and multipath, data scarcity for identification, cooperative multi-BS fusion, and real-world deployment constraints. Finally, we outline promising future research directions toward 5G-Advanced (5G-A) and 6G-enabled low-altitude surveillance systems.

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arXiv2026

Logistic-aided Huber M-estimator for robust GNSS positioning

This paper develops a logistic-aided Huber (LAH) M-estimator for robust GNSS positioning under long-tailed, multipath-affected measurement errors. The key idea is to leverage a logistic measurement error assumption and establish a one-to-one approximation between the logistic-based loglikelihood (i.e., quasi-log-cosh) and the Huber kernel by matching their score functions. This yields closed-form tuning rules for the scale and threshold parameters in the Huber estimator, grounded on logistic error statistical properties. We further show that the proposed LAH estimator preserves comparable efficiency and robustness to the connected logistic-based least quasi-log-cosh (LQLC) estimator. Both Monte Carlo simulations with long-tailed measurement errors and a one-hour urban GNSS dataset confirm that the proposed logistic-statistics-based tuning improves positioning accuracy and precision while suppressing large error spikes. Specifically, LAH reduces the 2D RMSE/STD by 28.03%/38.83% versus conventional 95%-efficiency-based Huber tuning in simulation, and reduces the overall 3D RMSE/STD by 4.85%/16.68% in real-world experiments while suppressing large positioning error spikes by up to 51%.

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arXiv2026

Improved GNSS Positioning in Urban Environments Using a Logistic Error Model

A Gaussian error assumption is commonly adopted in the pseudorange measurement model for global navigation satellite system (GNSS) positioning, which leads to the conventional least squares (LS) estimator. In urban environments, however, multipath and non-line-of-sight (NLOS) receptions produce heavy-tailed pseudorange errors that are not well represented by the Gaussian model. This study models urban GNSS pseudorange errors using a logistic distribution and derives the corresponding maximum likelihood estimator, termed the Least Quasi-Log-Cosh (LQLC) estimator. The resulting estimation problem is solved efficiently using an iteratively reweighted least squares (IRLS) algorithm. Experiments in light, medium, and deep urban environments show that LQLC consistently outperforms LS, reducing the three-dimensional (3D) root mean square error (RMSE) by approximately 11%-31% and the 3D error standard deviation (STD) by approximately 27%-61%. A controlled scale-mismatch analysis further shows that LQLC is more sensitive to severe underestimation than to overestimation of the logistic scale, indicating that the practical tuning requirement is to avoid overly small scale values rather than to achieve exact scale matching. In addition, the computational cost remains compatible with real-time positioning. These results indicate that logistic modeling provides a simple and practical alternative to Gaussian-based urban GNSS positioning.

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arXiv2026

Cauchy-Gaussian Overbound for Heavy-tailed GNSS Measurement Errors

Overbounds of heavy-tailed measurement errors are essential to meet stringent navigation requirements in integrity monitoring applications. This paper proposes to leverage the bounding sharpness of the Cauchy distribution in the core and the Overbounds of heavy-tailed measurement errors are essential for meeting stringent navigation requirements in integrity-monitoring applications. This paper proposes to leverage the bounding sharpness of the Cauchy distribution in the core and the Gaussian distribution in the tails to tightly bound heavy-tailedglobal navigation satellite system measurement errors. We develop a procedure to determine the overbounding parameters for both symmetric unimodal (SU)and non-symmetric unimodal (NSU) heavy-tailed errors and prove that the over-bounding property is preserved through convolution. Experiment results on both simulated and real-world data sets reveal that our method can sharply boundheavy-tailed errors in both the core and tail regions. In the position domain, the proposed method reduces the average vertical protection level by 15% for SU heavy-tailed errors compared with the single-cumulative-density-function Gaussian overbound and by 21%-47% for NSU heavy-tailed errors compared with the navigation discrete envelope and two-step Gaussian overbounds.

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arXiv2025

Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality

Estimating the absolute orientation of a local system relative to a global navigation satellite system (GNSS) reference often suffers from local minima and high dependency on satellite availability. Existing methods for this alignment task rely on abundant satellites unavailable in GNSS-degraded environments, or use local optimization methods which cannot guarantee the optimality of a solution. This work introduces a globally optimal solver that transforms raw pseudo-range or Doppler measurements into a convexly relaxed problem. The proposed method is certifiable, meaning it can numerically verify the correctness of the result, filling a gap where existing local optimizers fail. We first formulate the original frame alignment problem as a nonconvex quadratically constrained quadratic program (QCQP) problem and relax the QCQP problem to a concave Lagrangian dual problem that provides a lower cost bound for the original problem. Then we perform relaxation tightness and observability analysis to derive criteria for certifiable optimality of the solution. Finally, simulation and real world experiments are conducted to evaluate the proposed method. The experiments show that our method provides certifiably optimal solutions even with only 2 satellites with Doppler measurements and 2D vehicle motion, while the traditional velocity-based VOBA method and the advanced GVINS alignment technique may fail or converge to local optima without notice. To support the development of GNSS-based navigation techniques in robotics, all code and data are open-sourced at https://github.com/Baoshan-Song/Certifiable-Doppler-alignment.

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arXiv2025

Degeneration of Sliding-Window Factor Graph Optimization into Iterated Extended Kalman Filtering

Sliding window factor graph optimization (SW-FGO) is widely recognized for its robustness, yet its theoretical relationship with the extended Kalman filter (EKF) remains a subject of debate. This paper establishes the sufficient conditions to bridge SW-FGO with the iterated extended Kalman filter (IEKF). We introduce recursive FGO (Re-FGO), a conceptual perspective that employs a two-stage marginalization pipeline to mathematically degenerate the factor graph optimization to the IEKF recursive update. By enforcing the Markov assumption and a single-state window, we prove the theoretical equivalence between the IEKF and Re-FGO. This degeneration is validated through simulations and real-world urban GNSS and INS tightly coupled fusion experiments. The results confirm that Re-FGO exactly reproduces IEKF estimation behavior, demonstrating that the two-stage marginalization pipeline is foundational to enforce structural consistency, thereby successfully uniting graph-based smoothing and filtering paradigms under unified optimization principles.

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arXiv2025

Online IMU-odometer Calibration using GNSS Measurements for Autonomous Ground Vehicle Localization

Accurate calibration of intrinsic (odometer scaling factors) and extrinsic parameters (IMU-odometer translation and rotation) is essential for autonomous ground vehicle localization. Existing GNSS-aided approaches often rely on positioning results or raw measurements without ambiguity resolution, and their observability properties remain underexplored. This paper proposes a tightly coupled online calibration method that fuses IMU, odometer, and raw GNSS measurements (pseudo-range, carrier-phase, and Doppler) within an extendable factor graph optimization (FGO) framework, incorporating outlier mitigation and ambiguity resolution. Observability analysis reveals that two horizontal translation and three rotation parameters are observable under general motion, while vertical translation remains unobservable. Simulation and real-world experiments demonstrate superior calibration and localization performance over state-of-the-art loosely coupled methods. Specifically, the IMU-odometer positioning using our calibrated parameters achieves the absolute maximum error of 17.75 m while the one of LC method is 61.51 m, achieving up to 71.14 percent improvement. To foster further research, we also release the first open-source dataset that combines IMU, 2D odometer, and raw GNSS measurements from both rover and base stations.

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arXiv2025

Two stage GNSS outlier detection for factor graph optimization based GNSS-RTK/INS/odometer fusion

Reliable GNSS positioning in complex environments remains a critical challenge due to non-line-of-sight (NLOS) propagation, multipath effects, and frequent signal blockages. These effects can easily introduce large outliers into the raw pseudo-range measurements, which significantly degrade the performance of global navigation satellite system (GNSS) real-time kinematic (RTK) positioning and limit the effectiveness of tightly coupled GNSS-based integrated navigation system. To address this issue, we propose a two-stage outlier detection method and apply the method in a tightly coupled GNSS-RTK, inertial navigation system (INS), and odometer integration based on factor graph optimization (FGO). In the first stage, Doppler measurements are employed to detect pseudo-range outliers in a GNSS-only manner, since Doppler is less sensitive to multipath and NLOS effects compared with pseudo-range, making it a more stable reference for detecting sudden inconsistencies. In the second stage, pre-integrated inertial measurement units (IMU) and odometer constraints are used to generate predicted double-difference pseudo-range measurements, which enable a more refined identification and rejection of remaining outliers. By combining these two complementary stages, the system achieves improved robustness against both gross pseudo-range errors and degraded satellite measuring quality. The experimental results demonstrate that the two-stage detection framework significantly reduces the impact of pseudo-range outliers, and leads to improved positioning accuracy and consistency compared with representative baseline approaches. In the deep urban canyon test, the outlier mitigation method has limits the RMSE of GNSS-RTK/INS/odometer fusion from 0.52 m to 0.30 m, with 42.3% improvement.

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arXiv2025

Certifiably Optimal Doppler Positioning using Opportunistic LEO Satellites

To provide backup and augmentation to global navigation satellite system (GNSS), Doppler shift from Low Earth Orbit (LEO) satellites can be employed as signals of opportunity (SOP) for position, navigation and timing (PNT). Since the Doppler positioning problem is non-convex, local searching methods may produce two types of estimates: a global optimum without notice or a local optimum given an inexact initial estimate. As exact initialization is unavailable in some unknown environments, a guaranteed global optimization method in no need of initialization becomes necessary. To achieve this goal, we propose a certifiably optimal LEO Doppler positioning method by utilizing convex optimization. In this paper, the certifiable positioning method is implemented through a graduated weight approximation (GWA) algorithm and semidefinite programming (SDP) relaxation. To guarantee the optimality, we derive the necessary conditions for optimality in ideal noiseless cases and sufficient noise bounds conditions in noisy cases. Simulation and real tests are conducted to evaluate the effectiveness and robustness of the proposed method. Specially, the real test using Iridium-NEXT satellites shows that the proposed method estimates an certifiably optimal solution with an 3D positioning error of 140 m without initial estimates while Gauss-Newton and Dog-Leg are trapped in local optima when the initial point is equal or larger than 1000 km away from the ground truth. Moreover, the certifiable estimation can also be used as initialization in local searching methods to lower down the 3D positioning error to 130 m.

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arXiv2025

Credible Uncertainty Quantification under Noise and System Model Mismatch

State estimators often provide self-assessed uncertainty metrics, such as covariance matrices, whose credibility is critical for downstream tasks. However, these self-assessments can be misleading due to underlying modeling violations like noise model mismatch (NMM) or system model misspecification (SMM). This letter addresses this problem by developing a unified, multi-metric framework that integrates noncredibility index (NCI), negative log-likelihood (NLL), and energy score (ES) metrics, featuring an empirical location test (ELT) to detect system model bias and a directional probing technique that uses the metrics' asymmetric sensitivities to distinguish NMM from SMM. Monte Carlo simulations reveal that the proposed method achieves excellent diagnosis accuracy (80-100%) and significantly outperforms single-metric diagnosis methods. The effectiveness of the proposed method is further validated on a real-world UWB positioning dataset. This framework provides a practical tool for turning patterns of credibility indicators into actionable diagnoses of model deficiencies.

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arXiv2025

Jackknife ARAIM: Efficient GNSS Integrity Monitoring for Simultaneous Faults under Non-Gaussian Errors

Legacy and advanced receiver autonomous integrity monitoring (RAIM/ARAIM) rely on Gaussian error models that can be overly conservative for real-world non-Gaussian errors. This paper proposes an extended jackknife detector capable of detecting multiple simultaneous faults with non-Gaussian nominal errors. Furthermore, an integrity monitoring algorithm, jackknife ARAIM, is developed by systematically exploiting the properties of the jackknife detector in the range domain. We prove that the proposed method has equivalent monitoring performance with the solution separation (SS) ARAIM, but is significantly computationally efficient for single-fault cases with non-Gaussian nominal errors, while maintaining similar efficiency to SS ARAIM for multiple-fault cases. The proposed method is examined in worldwide simulations, with the nominal measurement error simulated based on authentic experimental data, which reveals different findings in existing research. In a single Global Positioning System (GPS) constellation setting, the proposed method can reduce the 99.5 percentile vertical protection level (VPL) below 45 m, outperforming 50 m VPL produced by the ARAIM algorithm using Gaussian nominal error models. In GPS-Galileo dual-constellation setting, while these Gaussian-based ARAIM algorithms suffer VPL inflation over 60 m due to Galileo's heavy-tailed errors, the proposed method maintains VPL below 40 m, achieving over 92% normal operations for 35 m Vertical Alert Limit. Moreover, we tentatively implement the SS ARAIM using non-Gaussian overbounds and compare it with the proposed Jackknife ARAIM method regarding computation efficiency. The proposed method achieves up to 59.4% reduction in median processing time compared to SS ARAIM in single-constellation scenarios.

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arXiv2025

An Efficient Detector for Faulty GNSS Measurements Detection With Non-Gaussian Noises

Fault detection is crucial to ensure the reliability of navigation systems. However, mainstream fault detection methods are developed based on Gaussian assumptions on nominal errors, while current attempts at non-Gaussian fault detection are either heuristic or lack rigorous statistical properties. The performance and reliability of these methods are challenged in real-world applications. This paper proposes the jackknife detector, a fault detection method tailored for linearized pseudorange-based positioning systems under non-Gaussian nominal errors. Specifically, by leveraging the jackknife technique, a test statistic is derived as a linear combination of measurement errors, eliminating the need for restrictive distributional assumptions while maintaining computational efficiency. A hypothesis test with the Bonferroni correction is then constructed to detect potential faults in measurements. Theoretical analysis proves the equivalence between the jackknife detector and the solution separation (SS) detector, while revealing the former's superior computational efficiency. Through a worldwide simulation and a real-world satellite clock anomaly detection experiment--both involving non-Gaussian nominal errors--the proposed jackknife detector demonstrates equivalent detection performance to the SS detector but achieves a fourfold improvement in computational efficiency. These results highlight the jackknife detector's substantial potential for real-time applications requiring robust and efficient fault detection in non-Gaussian noise environments.

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arXiv2024

Improving GNSS Positioning in Challenging Urban Areas by Digital Twin Database Correction

Accurate positioning technology is the foundation for industry and business applications. Although indoor and outdoor positioning techniques have been well studied separately, positioning performance in the intermediate period of changing the positioning environment is still challenging. This paper proposed a digital twin-aided positioning correction method for seamless positioning focusing on improving the receiver's outdoor positioning performance in urban areas, where the change of the positioning environment usually happens. The proposed algorithm will simulate the positioning solution for virtual receivers in a grid-based digital twin. Based on the simulated positioning solutions, a statistical model will be used to study the positioning characteristics and generate a correction information database for real receivers to improve their positioning performance. This algorithm has a low computation load on the receiver side and does not require a specially designed antenna, making it implementable for small-sized devices.

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arXiv2024

Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless Positioning

We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.

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arXiv2024

Tightly Joined Positioning and Control Model for Unmanned Aerial Vehicles Based on Factor Graph Optimization

The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous uncertainties arising from measurement noise, abrupt disturbances, and nonlinear dynamics. As a result, the navigation reliability of the UAV is significantly challenged in complex dynamic areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings in complex urban areas, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, this research proposes a **tightly joined positioning and control model (JPCM) based on factor graph optimization (FGO)**. In particular, the proposed JPCM combines sensor measurements from positioning and control constraints into a unified probabilistic factor graph. Specifically, the positioning measurements are formulated as the factors in the factor graph. In addition, the model predictive control (MPC) is also formulated as the additional factors in the factor graph. By solving the factor graph contributed by both the positioning-related factors and the MPC-based factors, the complementariness of positioning and control can be deeply exploited. Finally, we validate the effectiveness and resilience of the proposed method using a simulated quadrotor system which shows significantly improved trajectory following performance.

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