Hasil untuk "Canals and inland navigation. Waterways"

Gareth Wimpenny, Francisco Lázaro, Jan Šafár et al.

The very-high-frequency data exchange system (VDES) is an emerging maritime radio communication system that will pave the road for novel e-navigation applications. A key problem in e-navigation is that of data authentication: determining that the data originate from a trusted party and have not undergone changes after transmission. This work considers the authentication requirements in VDES, while considering the constraints typical of the maritime environment, and analyzes several possible solutions. The proposed solution is two-tiered, with the default approach relying on digital signatures in low-traffic areas where available wireless capacity is sufficient. For areas under the control of a shore station for which available wireless capacity is low, we consider a low-overhead authentication scheme using the timed efficient stream loss-tolerant authentication (TESLA) protocol to authenticate all shore-to-ship traffic. TESLA is particularly attractive for future-proof quantum-safe cryptography, offering increased authentication data under the conditions of the low-data-rate VDES.

Dan Navon, Ehud Rivlin, Hector Rotstein

Terrain-aided navigation, which combines radar altitude with a digital terrain map (DTM), was developed before the era of the Global Positioning System to prevent error growth resulting from inertial navigation. Recently, cameras and substantial computational power have become ubiquitous in flying platforms, prompting interest in studying whether the radar altimeter can be replaced by a visual sensor. This paper presents a novel approach to vision-based terrain-aided localization by revisiting the correspondence and DTM (C-DTM) problem. We demonstrate that we can simplify the C-DTM problem by dividing it into a structure-from-motion (SFM) problem and then anchoring the solution to the terrain. The SFM problem can be solved using existing techniques such as feature detection, matching, and triangulation wrapped with a bundle adjustment algorithm. Anchoring is achieved by matching the point cloud to the terrain using ray-tracing and a variation of the iterative closest point method. One of the advantages of this two-step approach is that an innovative outlier filtering scheme can be included between the two stages to enhance overall robustness. The resulting algorithm consistently demonstrated high precision and statistical independence in the presence of initial errors across various simulations. The impact of different filtering methods was also studied, showing an improvement of 50% compared with the unfiltered case. The new algorithm has the potential to improve localization in real-world scenarios, making it a suitable candidate for pairing with an inertial navigation system and a Kalman f ilter to construct a comprehensive navigation system.

Helena Calatrava, Adrià Gusi-Amigó, Floor Melman et al.

The robust interference mitigation (RIM) framework offers a promising solution to jamming attacks on global navigation satellite system (GNSS) receivers. By identifying interfered samples as outliers in the selected transform domain, RIM operates without relying on jamming waveform assumptions. This paper adapts RIM for GNSS snapshot architectures, assessing the impact of low-bit quantization on receiver performance under continuous wave (CW) and chirp interference. Using simulated data, snapshot RIM demonstrates significant improvements, achieving gains of 10, 20, and 35 dB in detected satellites for 2-, 4-, and 8-bit quantization in the presence of CW jamming. We also analyze the effect of quantization on the effective jammer-to-noise ratio, waveform distortion, and robust variance estimation. An experiment with realistic recordings shows that snapshot RIM achieves a 20-dB gain in the carrier-to-noise ratio over a professional receiver. Finally, a 24-h specifications test supports the feasibility of RIM integration in snapshot receivers with a maximum time-to-first-fix increase of 0.31 s.

Seongpil Cho, Jae Yong Lee, Jungwook Han

Marine radar is crucial for detecting objects near ships and ensuring safe navigation, and the performance of marine radar relies heavily on gain-tuning. However, the radar sensor lacks sufficient feature information to effectively distinguish desired objects from radar noise or clutter. Selecting an appropriate radar parameter based on environmental conditions is therefore crucial for generating a clutter-minimized radar image for navigation. In this paper, we propose an adaptive decision-making method to select an appropriate sensor parameter value. Through numerous field tests, we determined that the gain value requires frequent adjustment for uncrewed surface vehicle (USV) operation in real-sea environments. To streamline the process of finding the parameter for improved detection performance, we then present an automatic decision-making methodology that determines the appropriate sea clutter adjustment parameter based on the surrounding environmental conditions. We conclude by sharing field test results using this adaptive parameter estimation approach.

Alan Yang Tara Mina, Grace Gao

For a satellite navigation system, binary spreading codes with good autocor-relation and cross-correlation properties are critical for ensuring precise synchronization and tracking with minimal intrasystem interference. In this paper, we demonstrate that multiple instances of the spreading code design problem found in the literature may be cast as binary-constrained convex optimization problems. This approach enables new optimization methods that can exploit the convex structure of the problem. We demonstrate this approach using a block coordinate descent (BCD) method, which applies a convexity-exploiting branch-and-bound method to perform the block updates. With minimal tuning, the BCD method was able to identify Global Positioning System codes with better mean-squared correlation performance compared with the Gold codes and codes derived from a recently introduced natural evolution strategy.

Yingjie Hu, Wang Hu

This paper presents a novel constrained Factor Graph Optimization (FGO)-based approach for networked inertial navigation in pedestrian localization. To effectively mitigate the drift inherent in inertial navigation solutions, we incorporate kinematic constraints directly into the nonlinear optimization framework. Specifically, we utilize equality constraints, such as Zero-Velocity Updates (ZUPTs), and inequality constraints representing the maximum allowable distance between body-mounted Inertial Measurement Units (IMUs) based on human anatomical limitations. While equality constraints are straightforwardly integrated as error factors, inequality constraints cannot be explicitly represented in standard FGO formulations. To address this, we introduce a differentiable softmax-based penalty term in the FGO cost function to enforce inequality constraints smoothly and robustly. The proposed constrained FGO approach leverages temporal correlations across multiple epochs, resulting in optimal state trajectory estimates while consistently maintaining constraint satisfaction. Experimental results confirm that our method outperforms conventional Kalman filter approaches, demonstrating its effectiveness and robustness for pedestrian navigation.

Richard B. Langley

Welcome to the Summer 2024 issue of NAVIGATION. A hot topic in our PNT research community these days is the development of techniques to counter jamming and spoofing of global navigation satellite systems. While much of this work is classified, some of it appears in the open literature. And in this issue of NAVIGATION, we have several articles on the topic. We also have articles on the natural degradation of GNSS signals caused by the ionosphere, the problem of multipath, the use of GNSS to navigate other satellites, and a lot more.

Roohollah Parvizi, Shahrukh Khan, Alison F. Banwell et al.

This paper presents estimates of surface reflectivity (SR) over time of global navigation satellite system (GNSS) signals scattered from a partially frozen lake surface. A portable ground-based GNSS reflectometry sensor system that collects both scattered global positioning system L1 signals and independent validation data (lidar and camera) was deployed on the Lake Michigan waterfront in Chicago at a time when the lake surface was a mixture of ice and water. Lidar surface scans were merged with camera images and mapped, along with estimated reflection zones. For three satellites whose reflection points scan across ice and water over time, the relative SR and mean red intensity (differentiating ice from water) of camera pixels inside the first Fresnel zone were computed and shown to be correlated. This system concept will be used in the future for more complete mapping of phase changes of snow and ice in the cryosphere.

David Akhihiero, Uthman Olawoye, Shounak Das et al.

This paper presents a cooperative navigation architecture in a global navigation satellite system (GNSS)-denied subterranean environment using an unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) team. The main focus of this design is to prolong the UAV mission time by reducing the UAV payload, sensing, and computational elements. To accomplish this, the UGV handles the mapping of the environment, its own state estimation, and the state estimation of the UAV using the UAV’s proprioceptive sensors, a three-dimensional lidar, and an ultra-wideband ranging radio that communicates with a similar radio on the UAV. The UAV is assumed to be instrumented with an inertial measurement unit, stereo camera, and laser altimeter, and the data from these instruments are shared with the UGV over a local network for use in UAV state estimation. This paper presents the architecture for localization of a UAV/UGV team and realizes the implementation using two different nonlinear state estimators. Details and a comparison between an extended Kalman filter and an incremental factor graph optimization implementation are provided. The performance of the presented algorithms is analyzed via experiments conducted in a motion-capture facility.

Maximilian Simonetti, Omar García Crespillo

Vertical navigation is crucial for safe aircraft separation and has been traditionally based on the pressure altitude provided by barometric altimeters. New aviation operations require robust determination of geodetic altitude and are expected to primarily rely on a global navigation satellite system (GNSS). Because deviations between pressure and geodetic altitudes can reach hundreds of meters, an altitude harmonization is needed to use barometers in combination with GNSS. In this paper, we first present a methodology to compute an accurate geodetic altitude from barometer and external weather data. Secondly, we derive error and threat models of this geodetic altitude. Finally, we employ these models within a GNSS integrity monitoring algorithm augmented with the derived altitude. We assess our methodologies against flight test measurements and availability simulations of localizer performance with vertical guidance operations. These analyses illustrate the potential benefits of employing barometers as augmentation or stand-alone systems for geodetic altitude navigation.

Xiao Xia, Weisong Wen, Li-Ta Hsu

Global navigation satellite system (GNSS) integrity monitoring (IM) has been introduced in aviation, but remains challenging for urban scenarios because of limited satellite visibility and strong multipath and non-line-of-sight effects. Consequently, factors such as limited measurement redundancy and inaccurate uncertainty modeling significantly compromise positioning and IM performance. To alleviate these issues, this paper proposes an integrity-constrained factor graph optimization model for GNSS positioning augmented by switch variables. In contrast to conventional IM methods, this method enhances redundancy through the factor graph structure. Instead of directly excluding measurements, the proposed method reweights the measurements by using switch variables to satisfy a chi-square test constraint within the optimization, ultimately yielding optimal positioning accuracy. Moreover, a proper protection level that conservatively bounds the positioning error can be derived by using the modified weighting matrix under a single-fault assumption. The effectiveness of the proposed method was verified based on data sets collected in open-sky and urban-canyon areas in Hong Kong.

Marta Cortinovis, Keidai Iiyama, Grace Gao

Plans to establish a satellite network around the Moon to support communication, position, navigation, and timing services are rapidly evolving. Satellites that are part of this system broadcast their ephemeris as finite parameters to lunar users for user state estimation. In this work, we investigate lunar satellite ephemeris design to identify the optimal parameterization to broadcast to a lunar user. The proposed framework directly approximates the lunar satellite position and velocity in the inertial frame and obtains the conversion parameters necessary for state representation in the lunar fixed frame. The framework leverages signal-in-space-error requirements as constraints in the parameterization process to guide the search for the best ephemeris parameter set. We evaluate the performance of our proposed framework for satellites in a low lunar orbit and an elliptical lunar frozen orbit. The performance of different methods is assessed based on the precision of the ephemeris prediction, fit interval, and message size. We showcase the ability of the developed framework to approximate satellite ephemeris for both orbits to the desired precision by adjusting the fit interval and the number of parameters to broadcast. In particular, we demonstrate that formulations with a standard polynomial basis and a Chebyshev polynomial basis produce feasible solutions for ephemeris approximation at varying epochs in orbits, abiding by signal-in-space-error requirements.

Michael Felux, Patric Fol, Benoit Figuet et al.

Global navigation satellite systems have enabled significant improvements in aeronautical navigation. However, in recent years, a growing number of interference events have been reported by flight crews. In this paper, we first identify such events using crowd-sourced surveillance data collected between February and December 2022 for three different regions: the Baltic states, eastern Europe bordering the Black Sea, and the eastern Mediterranean. Then, we assess the extent and duration of these events to determine their impact on civil aviation. The analysis shows different characteristics, ranging from isolated events to regular large-scale and recurrent disruptions. Next, we identify aircraft types for the affected flights and evaluate flight plan data with respect to navigation equipment in order to identify flights that rely solely on satellite navigation and that might require assistance in the case of a loss of satellite navigation. Finally, we show the impact of radio frequency interference (RFI) on a selected passenger flight by analyzing automatic dependent surveillance-broadcast data as well as avionics data obtained from the airline’s flight data monitoring department for that specific flight and link the observations to the warnings triggered by the aircraft to alert the flight crew while encountering RFI.

Ernest Ofosu Addo, Wahid Elmarissi, Stefano Caizzone

Reference stations constitute important elements within the global navigation satellite system (GNSS) infrastructure, as they provide valuable measurements for performance monitoring. For high-quality measurements from such stations, local error sources should be properly characterized and compensated for or minimized. Multipath remains a major contributor to these errors. In severe occurrences, multipath can cause critical errors in sensitive systems such as those utilized for code-dependent applications. This paper discusses a method for GNSS multipath characterization in challenging installation scenarios, based on a dual-polarization antenna and its integration in a hybrid measurement–simulation framework. A dedicated dual-polarized probe, which houses both an effective geodetic antenna and a multipath-susceptible antenna, was designed, manufactured, and assessed. The dual-sensing nature of the probe allows auxiliary information to be acquired about multipath generated by nearby objects and can be used to infer a plausible range of expected multipath-induced code error at a GNSS sensor station. In addition, a ray-tracing method is discussed, in which antenna measurements are integrated into digital-twin simulations of installations for characterizing multipath conditions. Finally, this study demonstrates that by combining the DPA with digital-twin simulations, it is possible to predict multipath error bounds at an installation in advance. This combined technique presents a flexible tool that is useful for planning system performance with respect to multipath, site layout/selection, and even optimal receiving antenna placement at a given installation. The proposed simulative method is validated through field experiments, and tests with commercial geodetic-grade antennas are presented to confirm the capability of this method to predict their performance ranges.

Tara Mina, Ashwin Kanhere, Akshay Shetty et al.

To protect civilian Global Positioning System (GPS) users from spoofing, the Air Force Research Lab has developed the chips-message robust authentication (Chimera) signal enhancement for the GPS L1C signal. With Chimera, standalone receivers that only have access to the GPS signal will be able to authenticate their received measurements once every 3 min, whereas users with access to an out-of-band source will be able to perform authentication once every 1.5 or 6 s. However, moving receivers typically rely on much faster real-time GPS update rates of 1–20 Hz. In this work, we design a spoofing-resilient filter framework that provides continuous and secure state estimation between Chimera authentication times. By leveraging self-contained sensors on-board the vehicle, such as an inertial measurement unit or wheel encoder, as well as the periodic Chimera authentication, our proposed filter determines how much to rely on the received unauthenticated GPS measurements for state estimation. In this respect, our filter relies more extensively on GPS measurements in order to improve real-time navigation performance and reduce localization errors when GPS signals are authentic, while successfully mitigating spoofing-induced errors during an experienced attack. We experimentally validate our proposed spoofing-resilient filter in a simulated test environment for a ground vehicle model with access to the 3-min Chimera channel, under various simulated spoofing attack scenarios. To the best of the authors’ knowledge, this is the first adaptive filter proposed for Chimera that continuously leverages real-time GPS measurements in a spoofing-resilient manner.

Matthew Pierson, Zia Mehrabi

Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African continent. Our outputs provide detail of waterways structures hereto unmapped. When assessed against community needs requests for rural bridge building related to access to schools, health care facilities and agricultural markets, we find these newly generated waterways capture on average 93% (country range: 88-96%) of these requests whereas Open Street Map, and the state of the art data from TDX-Hydro, capture only 36% (5-72%) and 62% (37%-85%), respectively. Because these new machine learning enabled maps are built on public and operational data acquisition this approach offers promise for capturing humanitarian needs and planning for social development in places where cartographic efforts have so far failed to deliver. The improved performance in identifying community needs missed by existing data suggests significant value for rural infrastructure development and better targeting of development interventions.

Aaron Young, Nevindu M. Batagoda, Harry Zhang et al.

Robust autonomous navigation in environments with limited visibility remains a critical challenge in robotics. We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation. Our method enables mobile robots to "see around corners" by utilizing multi-bounce light information, effectively expanding their perceptual range without additional infrastructure. We propose a three-module pipeline: (1) Sensing, which captures multi-bounce histograms using SPAD-based LiDAR; (2) Perception, which estimates occupancy maps of hidden regions from these histograms using a convolutional neural network; and (3) Control, which allows a robot to follow safe paths based on the estimated occupancy. We evaluate our approach through simulations and real-world experiments on a mobile robot navigating an L-shaped corridor with hidden obstacles. Our work represents the first experimental demonstration of NLOS imaging for autonomous navigation, paving the way for safer and more efficient robotic systems operating in complex environments. We also contribute a novel dynamics-integrated transient rendering framework for simulating NLOS scenarios, facilitating future research in this domain.

Quanfeng Lu, Wenqi Shao, Zitao Liu et al.

Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing manual intervention. However, prior GUI agents often trained with datasets comprising tasks that can be completed within a single app, leading to poor performance in cross-app navigation. To address this problem, we present GUIOdyssey, a comprehensive dataset for cross-app mobile GUI navigation. GUIOdyssey comprises 8,334 episodes with an average of 15.3 steps per episode, covering 6 mobile devices, 212 distinct apps, and 1,357 app combinations. Each step is enriched with detailed semantic reasoning annotations, which aid the model in building cognitive processes and enhancing its reasoning abilities for complex cross-app tasks. Building on GUIOdyssey, we develop OdysseyAgent, an exploratory multimodal agent for long-step cross-app navigation equipped with a history resampler module that efficiently attends to historical screenshot tokens, balancing performance and inference speed. Extensive experiments conducted in both in-domain and out-of-domain scenarios validate the effectiveness of our approach. Moreover, we demonstrate that historial information involving actions, screenshots and context in our dataset can significantly enhances OdysseyAgent's performance on complex cross-app tasks.

Kathrin Donandt, Karim Böttger, Dirk Söffker

Accurate vessel trajectory prediction is necessary for save and efficient navigation. Deep learning-based prediction models, esp. encoder-decoders, are rarely applied to inland navigation specifically. Approaches from the maritime domain cannot directly be transferred to river navigation due to specific driving behavior influencing factors. Different encoder-decoder architectures, including a transformer encoder-decoder, are compared herein for predicting the next positions of inland vessels, given not only spatio-temporal information from AIS, but also river specific features. The results show that the reformulation of the regression task as classification problem and the inclusion of river specific features yield the lowest displacement errors. The standard LSTM encoder-decoder outperforms the transformer encoder-decoder for the data considered, but is computationally more expensive. In this study for the first time a transformer-based encoder-decoder model is applied to the problem of predicting the ship trajectory. Here, a feature vector using the river-specific context of navigation input parameters is established. Future studies can built on the proposed models, investigate the improvement of the computationally more efficient transformer, e.g. through further hyper-parameter optimization, and use additional river-specific information in the context representation to further increase prediction accuracy.

Xinshuai Song, Weixing Chen, Yang Liu et al.

Existing Vision-Language Navigation (VLN) methods primarily focus on single-stage navigation, limiting their effectiveness in multi-stage and long-horizon tasks within complex and dynamic environments. To address these limitations, we propose a novel VLN task, named Long-Horizon Vision-Language Navigation (LH-VLN), which emphasizes long-term planning and decision consistency across consecutive subtasks. Furthermore, to support LH-VLN, we develop an automated data generation platform NavGen, which constructs datasets with complex task structures and improves data utility through a bidirectional, multi-granularity generation approach. To accurately evaluate complex tasks, we construct the Long-Horizon Planning and Reasoning in VLN (LHPR-VLN) benchmark consisting of 3,260 tasks with an average of 150 task steps, serving as the first dataset specifically designed for the long-horizon vision-language navigation task. Furthermore, we propose Independent Success Rate (ISR), Conditional Success Rate (CSR), and CSR weight by Ground Truth (CGT) metrics, to provide fine-grained assessments of task completion. To improve model adaptability in complex tasks, we propose a novel Multi-Granularity Dynamic Memory (MGDM) module that integrates short-term memory blurring with long-term memory retrieval to enable flexible navigation in dynamic environments. Our platform, benchmark and method supply LH-VLN with a robust data generation pipeline, comprehensive model evaluation dataset, reasonable metrics, and a novel VLN model, establishing a foundational framework for advancing LH-VLN.