Search Results (86)

The precise locations of unknown non-cooperative targets are a long-standing technical problem that needs to be solved urgently in disaster relief and emergency rescue. An imaging model of photography to a non-cooperative target was established based on the binocular vision forward intersection. The collinear equation representing the spatial position relationship between the target and its two images was obtained through coordinate system transformation, and the system of equations to calculate the geographic coordinates of the target was derived, which realized the geo-location of the unknown non-cooperative target with no control points and no source. The composition and source of the error of this target location method were analyzed, and the equation to calculate the total error of the target location was obtained according to the error synthesis theory. The accuracy of the target location was predicted. When the elevation difference between the camera and the target is 3 km, the location accuracy is 15.5 m. The same ground target was imaged by a certain type of aerial camera at different locations 3097 m above ground, and a target location verification experiment was completed. The longitude and latitude of the target obtained were compared with the true geographic longitude and latitude, and the location error of the verification experiment was calculated to be 16.3 m. The research work of this paper provides a theoretical basis and methods for the precise locations of unknown non-cooperative targets and proposes specific measures to improve the accuracy of target location. Full article

Cross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately. To address these issues, we propose a Multi-Scale Cascade and Feature Adaptive Alignment (MCFA) network, which consists of a Multi-Scale Cascade Module (MSCM) and a Feature Adaptive Alignment Module (FAAM). The MSCM captures the features of the target’s adjacent regions and enhances the model’s robustness by learning key region information through association and fusion. The FAAM, with its dynamically weighted feature alignment module, adaptively adjusts feature differences across different viewpoints, achieving feature alignment between drone and satellite images. Our method achieves state-of-the-art (SOTA) performance on two public datasets, University-1652 and SUES-200. In generalization experiments, our model outperforms existing SOTA methods, with an average improvement of 1.52% in R@1 and 2.09% in AP, demonstrating its effectiveness and strong generalization in cross-view geo-localization tasks. Full article

In this work, we propose a geolocation framework for distant ground targets integrating laser rangefinder sensors with multimodal visual servo control. By simulating binocular visual servo measurements through monocular visual servo tracking at fixed time intervals, our approach requires only single-session sensor attitude correction calibration to accurately geolocalize multiple targets during a single flight, which significantly enhances operational efficiency in multi-target geolocation scenarios. We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. The experimental results show that through the UAV’s continuous reconnaissance measurements, the geolocalization error of remote ground targets based on our algorithm is less than 7 m for 3000 m, and less than 3.5 m for 1500 m. We have realized the fast and high-precision geolocalization of remote targets on the ground under the high-altitude reconnaissance of UAVs. Full article

Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., drone and satellite. Due to its GPS-free advantage, cross-view geo-localization is gaining increasing research interest, especially in drone-based localization and navigation applications. In order to guarantee system accuracy, existing methods mainly focused on image augmentation and denoising while still facing performance degradation when extreme weather conditions are considered. In this paper, we propose a robust end-to-end image retrieval framework, AGEN, serving for cross-view geo-localization under extreme weather conditions. Inspired by the strengths of the DINOv2 network, particularly its strong performance in global feature extraction, while acknowledging its limitations in capturing fine-grained details, we integrate the DINOv2 network with the Local Pattern Network (LPN) algorithm module to extract valuable classification features more efficiently. Additionally, to further enhance model robustness, we innovatively introduce an Adaptive Error Control (AEC) module based on fuzzy control to optimize the loss function dynamically. Specifically, by adjusting loss weights adaptively, the AEC module allows the model to better handle complex and challenging scenarios. Experimental results demonstrate that AGEN achieves a Recall@1 accuracy of 91.71% on the University160k-WX dataset under extreme weather conditions. Through extensive experiments on two well-known public datasets, i.e., University-1652 and SUES-200, AGEN achieves state-of-the-art Recall@1 accuracy in both drone-view target localization tasks and drone navigation tasks, outperforming existing models. In particular, on the University-1652 dataset, AGEN reaches 95.43% Recall@1 in the drone-view target localization task, showcasing its superior capability in handling challenging scenarios. Full article

The context of this study is the geolocation of signal emitters on the Earth’s surface through satellite platforms able to perform Angle of Arrival (AOA) measurements. This paper provides the theoretical framework to solve the optimization problem for the orbital deployment of the satellites minimizing the variance on the position error estimation with constraints on the line of sight (LOS). The problem is theoretically formulated for an arbitrary number of satellites in Low Earth Orbit (LEO) and target pointing attitude, focusing on minimizing the Position Dilution of Precision (PDOP) metric, providing a methodology for translating mission design requirements into problem formulation. An exemplary numerical application is presented for the operative case of the placement of a second satellite after a first one is launched. Simulation results are on angles of true anomaly, right ascension of the ascending node, and spacing angle, while accounting for orbital radius and emitter latitude. New insights on trends, parameter dependencies, and properties of symmetry and anti-symmetry are presented. The topic is of interest for new technological demonstrators based on CubeSats with AOA payload. Civil applications of interest are on interceptions of non-cooperative signals in activities of spectrum monitoring or search and rescue. Full article

The application of UAVs in surveillance, disaster management, and military operations has surged, necessitating robust and real-time tracking systems for moving targets. However, accurately tracking and predicting the trajectories of ground targets pose significant challenges due to factors such as target occlusion, varying speeds, and dynamic environments. To address these challenges and advance the capabilities of UAV-based tracking systems, a novel vision-based approach is introduced in this paper. This approach leverages the visual data captured by the UAV’s onboard cameras to achieve real-time tracking, geolocation, trajectory recovery, and predictive analysis of moving ground targets. By employing filter, regression and optimization techniques, the proposed system is capable of accurately estimating the target’s current position and predicting its future path even in complex scenarios. The core innovation of this research lies in the development of an integrated algorithm that combines object detection, target geolocation, and trajectory estimation into a single, cohesive framework. This algorithm not only facilitates the online recovery of the target’s motion trajectory but also enhances the UAV’s autonomy and decision-making capabilities. The proposed methods are validated through real flight experiments, demonstrating their effectiveness and feasibility. Full article

Safety, a critical component of sustainable development, necessitates an integrated approach in which urban planning assumes a central role. This study investigates the relationship between urban form and crime incidents in public spaces within the center of the city. This study was conducted in the city of Łódź, located in central Poland. Through geolocated data, this research explores crime incidents that, while not the most severe, disrupt public order and impact the overall quality of life. This study fills a gap in the existing literature by analyzing spatial variables such as urban vibrancy and the presence of alcohol outlets, alongside other urban elements. The analysis incorporates a variety of urban form variables, including land development indices, the functional layout of the urban neighborhood, pedestrian infrastructure, public space amenities, and facilities. Urban vibrancy, represented by the density of human activity, is also assessed in relation to crime incidents. The results indicate significant correlations between certain urban features and the occurrence of crime incidents, particularly the presence of public amenities and small businesses. While these findings suggest that urban design can influence crime rates, further panel and time-series regression analysis is needed to confirm these dynamics. Aligned with the 11th Sustainable Development Goal, this study provides insights that could inform urban planning strategies, offering recommendations to enhance both the functionality and safety of city centers. By understanding how urban design elements contribute to public safety, policymakers can develop more effective and targeted spatial planning strategies that promote not only aesthetics and functionality but also the well-being and security of residents. Full article

This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation algorithm with hierarchical filtering is proposed, and this algorithm performs motion state estimation for optoelectronic loads and ground-motion targets separately. Using the laser range sensor measurements as constraints, the optoelectronic load angle state quantities are involved together in estimating the ground target motion state, resulting in improved accuracy of ground-motion target localization tracking and motion state estimation. The experimental data show that the UAV ground-motion target localization tracking and motion estimation algorithm using hierarchical filtering reduces the localization tracking error by at least 7.5 m and the motion state estimation error by at least 0.8 m/s compared to other algorithms. Full article

Aquaculture is a globally widespread practice and the world’s fastest-growing food sector and requires technological advances to both increase productivity and minimize environmental impacts. Monitoring the sector is one of the priorities of state governments, international organizations, such as the Food and Agriculture Organization of the United States (FAO), and the European Commission. Data collection in aquaculture, particularly information on the location, number, and size of production facilities, is challenging due to the time required, the extent of the area to be monitored, the frequent changes in farming infrastructures and licenses, and the lack of automated tools. Such information is usually obtained through direct communications (e.g., phone calls and e-mails) with aquaculture producers and is rarely confirmed with on-site measurements. This study describes an innovative and automated method to obtain data on the number and placement of structures for marine and freshwater finfish farming through a YOLOv4 model trained on high-resolution images. High-resolution images were extracted from Google Maps to test their use with the YOLO model for the identification and geolocation of both land (raceways used in salmonids farming) and sea-based (floating sea cages used in seabream, seabass, and meagre farming) aquaculture systems in Italy. An overall accuracy of approximately 85% of correct object recognition of the target class was achieved. Model accuracy was tested with a dataset that includes images from Tuscany (Italy), where all these farm typologies are represented. The results demonstrate that the approach proposed can identify, characterize, and geolocate sea- and land-based aquaculture structures without performing any post-processing procedure, by directly applying customized deep learning and artificial intelligence algorithms. Full article

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments. Full article

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km. Full article

The JamSail mission is an educational CubeSat aiming to design, develop, and demonstrate two new technologies on a small satellite, tentatively scheduled for launch no earlier than 2026. When launched, JamSail will demonstrate the functionality of two new payloads in low Earth orbit. First, a flexible, low-cost GNSS interference detection payload capable of characterising and geolocating the sources of radio interference regarding the E1/L1 and E5a/L5 bands will be demonstrated on a global scale. The data produced by this payload can be used to target anti-interference actions in specific regions and aid in the design of future GNSS receivers to better mitigate specific types of interference. If successful, the flexibility of the payload will allow it to be remotely reconfigured in orbit to investigate additional uses of the technology, including a potential demonstration of GNSS reflectometry aboard a CubeSat. Second, a compact refractive solar sail will be deployed that is capable of adjusting the orbit of JamSail in the absence of an on-board propellant. This sail will be used to gradually raise the semi-major axis of JamSail over the span of the mission before being used to perform rapid passive deorbit near the end-of-life juncture. Additionally, self-stabilising optical elements within the sail will be used to demonstrate a novel method of performing attitude control. JamSail is currently in the testing phase, and the payloads will continue to be refined until the end of 2024. This paper discusses the key objectives of the JamSail mission, the design of the payloads, the expected outcomes of the mission, and future opportunities regarding the technologies as a whole. Full article

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation. Full article

Unmanned aerial vehicles (UAVs) have been employed to perform aerial surveys in many industries owing to their versatility, relatively low cost, and efficiency. Ground control points (GCPs) are used for georeferencing to ensure orthophoto geolocation/positioning accuracy. In this study, we investigate the impact of the number and distribution of GCPs on the accuracy of orthophoto production based on images acquired by UAVs. A test site was selected based on regulatory requirements, and several scenarios were developed considering the specifications of the UAVs used in this study. The locations of GCPs were varied to obtain the results. Based on the results obtained for different numbers of GCPs per unit area and distribution of GCPs, it is shown that UAV-based platforms can be more extensively utilized in a range of applications. The findings of this study will significantly impact the development process of GCP automation algorithms and enable a more cost-effective approach when determining target sites for UAV-based orthophoto production. Full article

Cross-view geolocation is a valuable yet challenging task. In practical applications, the images targeted by cross-view geolocation technology encompass multi-domain remote sensing images, including those from different platforms (e.g., drone cameras and satellites), different perspectives (e.g., nadir and oblique), and different temporal conditions (e.g., various seasons and weather conditions). Based on the characteristics of these images, we have designed an effective framework, Image Reconstruction and Multi-Unit Mutual Learning Net (IML-Net), for accomplishing cross-view geolocation tasks. By incorporating a deconvolutional network into the architecture to reconstruct images, we can better bridge the differences in remote sensing image features across different domains. This enables the mapping of target images from different platforms and perspectives into a shared latent space representation, obtaining more discriminative feature descriptors. The process enhances the robustness of feature extraction for locating targets across a wide range of perspectives. To improve the network’s performance, we introduce attention regions learned from different units as augmented data during the training process. For the current cross-view geolocation datasets, the use of large-scale datasets is limited due to high costs and privacy concerns, leading to the prevalent use of simulated data. However, real data allow the network to learn more generalizable features. To make the model more robust and stable, we collected two groups of multi-domain datasets from the Zurich and Harbin regions, incorporating real data into the cross-view geolocation task to construct the ZHcity750 Dataset. Our framework is evaluated on the cross-domain ZHcity750 Dataset, which shows competitive results compared to state-of-the-art methods. Full article