Hasil untuk "Canals and inland navigation. Waterways"

Muhammad Asad Ullah, Davi Brilhante, Luís Eduardo Partichelli Potrich et al.

Sixth-generation (6G) communication systems are expected to support direct-to-device (D2D) connectivity, enabling standard user equipment (UE) to seamlessly transition to non-terrestrial network (NTN), particularly satellite communication mode, when operating beyond terrestrial network (TN) coverage. This D2D concept does not require hardware modifications to conventional UEs and eliminates the need for dedicated satellite ground terminals. D2D-capable UEs can be mounted on both manned and unmanned aircraft, however, they are especially well-suited for low-altitude unmanned aircraft due to their compact form factor, lightweight design, energy efficiency, and TN-NTN roaming capabilities. D2D can also enable beyond-visual-line-of-sight operation by providing NTN support for Communications, Navigation, and Surveillance (CNS) services during TN outages or congestion. This paper investigates the capabilities and limitations of D2D connectivity for low-altitude unmanned aircraft operating in urban environments. We analyze the variation in line-of-sight probability for both TN and NTN links as a function of aircraft altitude. We further compute path loss and received signal strength while accounting for a representative TN deployment with down-tilted antennas. The results show that the TN and NTN links complement each other, significantly improving the availability of the CNS service at low altitudes. These findings provide insights to support the design and optimization of future 6G-enabled integrated CNS services.

Mingi Jeong, Alberto Quattrini Li

We present RENEW, a global path planner for Autonomous Surface Vehicle (ASV) in dynamic environments with external disturbances (e.g., water currents). RENEW introduces a unified risk- and energy-aware strategy that ensures safety by dynamically identifying non-navigable regions and enforcing adaptive safety constraints. Inspired by maritime contingency planning, it employs a best-effort strategy to maintain control under adverse conditions. The hierarchical architecture combines high-level constrained triangulation for topological diversity with low-level trajectory optimization within safe corridors. Validated with real-world ocean data, RENEW is the first framework to jointly address adaptive non-navigability and topological path diversity for robust maritime navigation.

Penggao Yan, Zhengdao Li, Feng Huang et al.

Fault detection is crucial to ensure the reliability of localization systems. However, conventional fault detection methods usually assume that noises in the system have a Gaussian distribution, limiting their effectiveness in real-world applications. This study proposes a fault detection algorithm for an extended Kalman filter (EKF)-based localization system by modeling non-Gaussian noises as a Gaussian mixture model (GMM). The relationship between GMM-distributed noises and the measurement residual is rigorously established through error propagation, which is utilized to construct the test statistic for a chi-squared test. The proposed method is applied to an EKF-based two-dimensional light detection and ranging/inertial measurement unit integrated localization system. Experimental results in a simulated urban environment show that the proposed method exhibits a 30% improvement in the detection rate and a 17%–23% reduction in the detection delay, compared with the conventional method with Gaussian noise modeling.

Tucker Haydon, Andy Huang, Todd E. Humphreys

The trusted inertial terrain-aided navigation (TITAN) algorithm leverages an airborne vertical synthetic aperture radar to measure the range to the closest ground points along several prescribed iso-Doppler contours. These TITAN minimum-range, prescribed-Doppler measurements are the result of a constrained nonlinear optimization problem whose optimization function and constraints both depend on the radar position and velocity. Owing to the complexity of this measurement definition, analysis of the TITAN algorithm is lacking in prior work. This publication offers such an analysis, making the following three contributions: (1) an analytical solution to the TITAN constrained optimization measurement problem, (2) a derivation of the TITAN measurement function Jacobian, and (3) a derivation of the Cramér-Rao lower bound on the estimated position and velocity error covariance. These three contributions are verified via Monte Carlo simulations over synthetic terrain, which further reveal two remarkable properties of the TITAN algorithm: (1) the along-track positioning errors tend to be smaller than the cross-track positioning errors, and (2) the cross-track positioning errors are independent of the terrain roughness.

Young-Jin Song, Ki-Ho Kwon, Jong-Hoon Won

The utilization of global navigation satellite systems (GNSSs) in the space service volume, such as the geostationary Earth orbit (GEO) altitude, has recently attracted significant interest owing to their potential advantages in performance and cost. Because the acquisition of the satellite signal represents a fundamental function of a GNSS receiver, the expected amount of time for successful acquisition, or mean acquisition time (MAT), as well as the acquisition performance itself, must be analyzed. Owing to the limited number of satellites and poor geometry at the GEO altitude, GNSS receivers often utilize signals originating from the sidelobes of the transmitting antenna pattern, which results in a weak signal power and a high Doppler shift. This paper presents a research methodology for a comprehensive analysis of acquisition time, with a particular focus on operations at the GEO altitude, considering the utilization of sidelobe signals. A generalized mathematical and probabilistic model is provided for the acquisition performance and MAT analysis of multi-constellation and multi-frequency signals. In particular, a realistic MAT model is proposed, based on a detailed computational performance analysis of the acquisition algorithm applied to an actual spaceborne receiver. A geometric simulation was conducted using the publicly available antenna patterns of the Global Positioning System (GPS), Galileo, and Quasi-Zenith Satellite System to incorporate orbital and signal characteristics into the determination of the search space. A method is proposed to modify the antenna patterns for other systems whose patterns are not publicly available, while preserving the sidelobe characteristics. Based on realistic scenarios and receiver parameters, acquisition-related analysis results for cold and warm starts, including the search space, dwell time, and MAT for GEO altitude receivers, are provided. The methods and results were verified through a Monte Carlo simulation configured via a software simulator and receiver pair.

Jeremy Ovadia, David Champlin, Corey Pong et al.

Accurate estimations of differential code biases (DCBs) are critical for producing absolute ionospheric measurements using global navigation satellite system observables. DCB estimation generally requires translating slant total electronic content (TEC) measurements to the vertical domain using a mapping function. Analyzing DCB estimates from regional modeling and global ionospheric maps (GIMs) over 4.5 years reveals significant DCB differences across different mapping functions, varying by a few nanoseconds. Decompositions of receiver DCB estimates show that for some mapping functions, such as the Jet Propulsion Lab extended slab function and the 350-km thin shell function, variations in DCB estimate over time can be largely accounted for by temperature and ionospheric activity. In contrast, other mapping functions, such as the 450-km thin shell function, exhibit significantly less variation over time. Our computational and analytical results suggest the importance of selecting an appropriate mapping function for accurate DCB and TEC estimation from GIMs and shell-based spatiotemporal models.

Sharbel Kozhaya, Joe Saroufim, Zaher (Zak) M. Kassas

This paper provides a comprehensive theoretical and experimental description of how to exploit Starlink low Earth orbit (LEO) satellites for positioning, navigation, and timing (PNT). First, the paper reveals for the first time, the full Starlink orthogonal frequency division multiplexing (OFDM) beacon, which spans the whole time-frequency resource grid. This description of the beacon is achieved through blind beacon estimation, which shows that the Starlink sequences published in the literature only comprise 0.66% of Starlink’s full OFDM. Exploiting this full OFDM beacon is shown to increase the receiver’s process gain by nearly 18 dB compared to only using signals published in the literature. This process gain, in turn, unlocks higher effective SNR at the receiver’s correlator output, enabling reliable acquisition and tracking in low SNR regimes imposed by using low-gain antennas. Second, the paper studies and compares the maximum achievable received carrier-to-noise density ratio (C/N0) for different reception scenarios. Third, the paper shows the first experimental results of navigation observables extracted using OFDM signals transmitted by Starlink satellites, namely the carrier phase, Doppler shift, and code phase. The paper provides the most comprehensive Starlink signal collection from 2021 through 2024 and analyzes the quality of pilot-tone versus OFDM-based observables. Results show that step-like corrections sometimes contaminate all the OFDM-based navigation observables from Starlink satellites, rendering their raw integration a challenge for precise positioning. Fourth, the paper shows how corrections made to the OFDM carrier frequency offset (CFO) can be estimated on-the-fly with a good degree of fidelity within the tracking loop of the software-defined receiver. Unlike the CFO corrections, the estimation of code phase corrections is shown to be intractable, rendering pseudoranges from Starlink signals not suitable for reliable positioning. Moreover, the tracked OFDM carrier phase revealed excessive slips due to the employed communication scheme. Finally, the paper demonstrates the first positioning solution that uses OFDM-based Doppler shift exclusively. Four positioning frameworks are formulated and assessed: (i) pilot tone-based Doppler shift tracking that exhibits no sign of contamination from the OFDM-related corrections, (ii) OFDM-based Doppler shift with uncorrected CFOs, (iii) OFDM-based Doppler shift with corrected CFOs that are estimated on-the-fly, and (iv) OFDM-based Doppler shift with corrected CFOs that are estimated using the knowledge of an assumed cooperative base station. The unprecedented results from these analyses show that, with an average of only three active Starlink satellites, a positioning solution with a 3D position estimation error of two meters can be achieved in only 20 seconds.

Beixi Chen, Kan Wang, Ahmed El-Mowafy et al.

Real-time, high-accuracy orbital products for low Earth orbit (LEO) satellites are essential for LEO-augmented real-time positioning, navigation and timing services. In particular, complete and continuous global navigation satellite system (GNSS) observations onboard tracked LEO satellites are necessary to guarantee precise orbit determination (POD) and generate short-term predicted orbits that can be fit with real-time ephemeris parameters. However, in practice, GNSS observations of LEO satellites often suffer from discontinuities due to tracking problems, data transmission problems, or downlinking strategies. Understanding the effect of these observation gaps on orbit accuracy is therefore essential for developing strategies to minimize accuracy degradation in real-time LEO satellite orbits. This study investigates trade-offs between two suites of strategies for addressing multi-hour observation data gaps followed by short segments of tail data during reduced-dynamic POD. The first strategy, EP, involves sacrificing the tail data and extending the prediction time. The second set of strategies retain the tail data but vary the POD strategies: the tested options include maintaining stochastic accelerations as estimable parameters (RP), not estimating stochastic accelerations (CP), or combining the RP-based orbits from the non-gap periods with the CP-based orbits during the gap (BP). Using real GNSS observations from the LEO satellite Sentinel-6A, we evaluated the accuracy and integrity of these strategies for 1-h orbital predictions with assumed gap lengths of 3, 5, 7, and 9 h and tail data lengths set to 15, 30, 45, and 60 min. Results show that the BP strategy achieves the highest prediction accuracy, with mean orbital user range errors (OUREs) of approximately 5.7 and 13.4 cm for a 3-h data gap followed by 60-min and 15-min tails, respectively. In contrast, the EP strategy demonstrates the highest integrity. For a 15-min tail, the 99.9% confidence level of the OURE for the EP strategy reaches approximately 3.1 and 8.7 dm for gap lengths of 3 h and 9 h, respectively. Overall, BP is the preferred strategy for maximizing prediction accuracy, while the EP strategy is preferable for short gaps and tails. The CP strategy provides a balanced approach, maintaining reasonably strong performance for both prediction accuracy and integrity.

Siddharth Kothari, Srinivasan Murali, Sankalp Kothari et al.

Inland water body segmentation from Synthetic Aperture Radar (SAR) images is an important task needed for several applications, such as flood mapping. While SAR sensors capture data in all-weather conditions as high-resolution images, differentiating water and water-like surfaces from SAR images is not straightforward. Inland water bodies, such as large river basins, have complex geometry, which adds to the challenge of segmentation. U-Net is a widely used deep learning model for land-water segmentation of SAR images. In practice, manual annotation is often used to generate the corresponding water masks as ground truth. Manual annotation of the images is prone to label noise owing to data poisoning attacks, especially due to complex geometry. In this work, we simulate manual errors in the form of adversarial attacks on the U-Net model and study the robustness of the model to human errors in annotation. Our results indicate that U-Net can tolerate a certain level of corruption before its performance drops significantly. This finding highlights the crucial role that the quality of manual annotations plays in determining the effectiveness of the segmentation model. The code and the new dataset, along with adversarial examples for robust training, are publicly available. (GitHub link - https://github.com/GVCL/IWSeg-SAR-Poison.git)

Abhishek Tyagi, Charu Gaur

We present an autonomous aerial surveillance platform, Veg, designed as a fault-tolerant quadcopter system that integrates visual SLAM for GPS-independent navigation, advanced control architecture for dynamic stability, and embedded vision modules for real-time object and face recognition. The platform features a cascaded control design with an LQR inner-loop and PD outer-loop trajectory control. It leverages ORB-SLAM3 for 6-DoF localization and loop closure, and supports waypoint-based navigation through Dijkstra path planning over SLAM-derived maps. A real-time Failure Detection and Identification (FDI) system detects rotor faults and executes emergency landing through re-routing. The embedded vision system, based on a lightweight CNN and PCA, enables onboard object detection and face recognition with high precision. The drone operates fully onboard using a Raspberry Pi 4 and Arduino Nano, validated through simulations and real-world testing. This work consolidates real-time localization, fault recovery, and embedded AI on a single platform suitable for constrained environments.

Amir Allahvirdi-Zadeh, Ahmed El-Mowafy

CubeSats hold promise for various applications, but their viability in demanding missions such as future low Earth orbiting position, navigation, and timing (LEO-PNT) systems hinges on higher orbital accuracy and reliable attitude information. To address these challenges, we present an array-aided combined precise orbit and attitude determination model with an optimal solution. In the estimation process, multi- and affine-constrained models are used to precisely determine the attitude, and then, highly precise observations for an antenna array are reconstructed based on fixed ambiguities and a decorrelation step. Validations confirm the significance of integer ambiguities in the model, highlighting the cost-effectiveness of this model compared with star trackers for attitude determination. The reconstructed observations outperform the original observations, leading to improved orbital components, with the three-dimensional root mean square (RMS) equal to 4.1 cm. The observation residuals are smoother, with an RMS of 6 mm, half of that obtained via a single antenna. The developed models offer great potential for CubeSats, advancing their orbit and attitude determination capabilities.

Birendra Kujur, Samer Khanafseh, Boris Pervan

In this article, we describe a new method for detecting global navigation satellite system (GNSS) spoofing using an inertial navigation system. We specifically address the most difficult-to-detect scenario, in which a spoofer replicates the authentic GNSS signal with only additive errors due to the spoofer’s uncertainty in knowledge of the target’s position. We derive an optimal monitor to detect the anomalous temporal structure of the spoofed measurements caused by the spoofer’s target tracking errors. This new monitor uses accumulated Kalman filter innovations projected into the position state domain. We demonstrate how the monitor window length can be set to achieve any required missed detection probability, and we evaluate the performance of the monitor for both white and colored tracking error. Finally, we present a complementary solution separation monitoring concept to detect rapid-onset spoofing and to achieve protection levels in real time.

Christopher Flood, Penina Axelrad

Recognition of the critical importance of positioning, navigation, and timing to all economic sectors is driving the development of diverse alternatives to global navigation satellite systems (GNSSs), termed AltPNT. One promising approach is to leverage the proliferation of small satellite constellations in low Earth orbit (LEO) to deliver GNSS augmentation services. The generation of one-way ranging signals suitable for AltPNT requires stable timing, accurately referenced to a common timescale such as Global Positioning System Time or Coordinated Universal Time. This paper describes a small-scale laboratory demonstration of an approach for cooperatively realizing a local timescale using low-size, weight, and power clocks distributed across multiple platforms, with no dependence on a GNSS. The demonstration is based on four interconnected software-defined radios to represent a four-satellite subset of a LEO constellation. Lab results show how each platform can generate a common timescale, with stability benefiting from all reference clocks.

Jason H. Rife, Matthew McDermott

This paper quantifies an error source that limits the accuracy of lidar scan matching, particularly for voxel-based methods. Lidar scan matching, which is used in dead reckoning (also known as lidar odometry) and mapping, computes the rotation and translation that best align a pair of point clouds. Perspective errors occur when a scene is viewed from different angles, with different surfaces becoming visible or occluded from each viewpoint. To explain perspective anomalies observed in data, this paper models perspective errors for two objects representative of urban landscapes: a cylindrical column and a dual-wall corner. For each object, we provide an analytical model of the perspective error for voxel-based lidar scan matching. We then analyze how perspective errors accumulate as a lidar-equipped vehicle moves past these objects.

Runwei Guan, Liye Jia, Fengyufan Yang et al.

The perception of waterways based on human intent is significant for autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in water environments. Inspired by visual grounding, we introduce WaterVG, the first visual grounding dataset designed for USV-based waterway perception based on human prompts. WaterVG encompasses prompts describing multiple targets, with annotations at the instance level including bounding boxes and masks. Notably, WaterVG includes 11,568 samples with 34,987 referred targets, whose prompts integrates both visual and radar characteristics. The pattern of text-guided two sensors equips a finer granularity of text prompts with visual and radar features of referred targets. Moreover, we propose a low-power visual grounding model, Potamoi, which is a multi-task model with a well-designed Phased Heterogeneous Modality Fusion (PHMF) mode, including Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). Exactly, ARW extracts required radar features to fuse with vision for prompt alignment. MHSCA is an efficient fusion module with a remarkably small parameter count and FLOPs, elegantly fusing scenario context captured by two sensors with linguistic features, which performs expressively on visual grounding tasks. Comprehensive experiments and evaluations have been conducted on WaterVG, where our Potamoi archives state-of-the-art performances compared with counterparts.

Zecheng Yin, Chonghao Cheng, and Yao Guo et al.

Navigating towards fully open language goals and exploring open scenes in an intelligent way have always raised significant challenges. Recently, Vision Language Models (VLMs) have demonstrated remarkable capabilities to reason with both language and visual data. Although many works have focused on leveraging VLMs for navigation in open scenes, they often require high computational cost, rely on object-centric approaches, or depend on environmental priors in detailed human instructions. We introduce Navigation with VLM (NavVLM), a training-free framework that harnesses open-source VLMs to enable robots to navigate effectively, even for human-friendly language goal such as abstract places, actions, or specific objects in open scenes. NavVLM leverages the VLM as its cognitive core to perceive environmental information and constantly provides exploration guidance achieving intelligent navigation with only a neat target rather than a detailed instruction with environment prior. We evaluated and validated NavVLM in both simulation and real-world experiments. In simulation, our framework achieves state-of-the-art performance in Success weighted by Path Length (SPL) on object-specifc tasks in richly detailed environments from Matterport 3D (MP3D), Habitat Matterport 3D (HM3D) and Gibson. With navigation episode reported, NavVLM demonstrates the capabilities to navigate towards any open-set languages. In real-world validation, we validated our framework's effectiveness in real-world robot at indoor scene.

Christian Tiberius, Gerard Janssen, Jeroen Koelemeij et al.

This paper presents a terrestrial networked positioning system that obtains a reliable time reference from a national time scale realization and distributes it in a prototype to six roadside base stations through a fiber-optic Gigabit Ethernet network. Wireless wideband signals are transmitted by the base stations, thereby enabling positioning by a mobile receiver with an accuracy of one decimeter in a multipath urban environment. The scalability and compatibility of this system with existing telecommunication-network technologies paves the way for wide-area global navigation satellite system-independent back-up systems for timing and positioning with improved coverage and performance. The results presented in this paper are based on research carried out within the scope of a project funded by the Dutch Research Council (NWO, project 13970).

Wang Li, Yiping Jiang

In ground-based augmentation system (GBAS) approach service type D (GAST-D), various ionospheric monitors are implemented in both aircraft and ground facilities to detect ionospheric anomalies. Additionally, the largest undetected differential range errors induced by ionospheric anomalies must be examined because these errors are used in geometry screening to identify potentially unsafe satellite geometries. Based on the ionospheric front threat model, a closed-form expression of the largest undetected ionospheric range error has been established for GBAS approach service type C (GAST-C), where only ground ionospheric monitoring is involved. This paper presents a closed-form expression for GAST-D, and both the ionospheric front model and plasma bubble threat model are taken into consideration. Based on exhaustive simulations among all possible ionospheric threat conditions, the expression is determined as a linear function of the relative speed and gradient magnitude of the ionospheric anomaly. Compared with the linear expression of ionospheric errors for GAST-C, the expression for GAST-D demonstrates that the use of additional ionospheric monitors and a smaller time constant for the code-carrier smoothing filter can effectively reduce the largest undetected ionospheric range error.

Sudha Vana, Sunil Bisnath

In this research, a next-generation, low-cost triple-frequency GNSS, microelectromechanical (MEMS) based inertial measurement unit (IMU), and a patch antenna was used to obtain decimeter-level accuracy in a suburban and urban environment. A unique combination of the low-cost hardware and software constraining was used to bridge the GNSS gaps in an urban environment to provide a continuous, accurate, and reliable position solution that is novel and has not been previously published. The low-cost navigation system demonstrates less than a decimeter-level accuracy in the presence of a sufficient number of satellites. During half a minute of introduced GNSS signal loss, the overall rms of the algorithm is 10–40% better than dual-frequency PPP with IMU, as the satellite availability reduces. The results obtained during partial GNSS availability indicate a significant step forward in the low-cost navigation area for applications like low-cost autonomous vehicles, intelligent transportation systems, etc. that demand a decimeter level of accuracy.

Hoi-Fung Ng, Li-Ta Hsu, Guohao Zhang

Global navigation satellite system (GNSS) signal reflection over buildings degrades positioning performance in urban canyons. Different three-dimensional (3D) mapping-aided (3DMA) GNSS algorithms have been proposed, which utilize 3D building models to aid in positioning. Recently, the candidate-based 3DMA GNSS framework has been applied to examine evenly spaced distributed particles. The particles that best match the observed measurements, that is, with the minimum cost, are identified as the receiver location. However, such particle sampling approaches incur a high computational load and are not robust. In this study, a Kriging-based interpolation method is applied to model the cost function of a 3DMA GNSS based on sampled particles, and the modeled cost function is then integrated with Doppler measurements through factor graph optimization. The regressed model can reduce the computational load by sparsely distributing the particles. Designed experiments with smartphone and commercial-level GNSS receivers demonstrate that the positioning performance can achieve a root mean square error of less than 10 m in Hong Kong and New York City urban canyons.