Search Results (235)

Unmanned Aerial Vehicles (UAVs) have become critical platforms for missions such as surveillance, reconnaissance, and target tracking, which require real-time decision-making, reliable sensing, and efficient resource utilization. However, limited onboard computing capacity, energy constraints, variable terrain conditions, and situations where targets are partially or fully obscured limit the performance of traditional fixed-configuration sensing and tracking approaches. In this study, we propose a context-aware and adaptive UAV-based target detection and tracking framework that dynamically selects the most appropriate detection and tracking algorithm by jointly evaluating terrain characteristics and mission requirements. The proposed system includes a three-stage terrain analysis module supported by HSV color space filtering, Canny edge detection, Laplacian texture variance, and contrast-based features. In cases where color-based classification is insufficient, Random Forest-based classification is used to distinguish between vegetation, bare ground, and urban areas; the terrain classification model achieves approximately 90% accuracy during the training and testing process. In the target detection phase, a YOLOv11-based model was trained on a specialized tank dataset created from various sources and labeled in YOLO format, achieving an mAP50 performance of approximately 85%. In the tracking phase, single-object and multi-object tracking algorithms are selected via a scoring-based decision mechanism depending on the terrain type and mission scenario. Additionally, a hybrid anomaly detection mechanism that evaluates target loss, sudden bounding box changes, and view inconsistencies was integrated into the system, thereby enhancing tracking reliability and enabling the re-detection or algorithm switching process when necessary. Experimental results demonstrate that the proposed context-aware approach can reduce computational load while maintaining tracking robustness under various environmental conditions. These findings highlight that environmental awareness and adaptive algorithm selection can make significant contributions to autonomy, operational efficiency, and real-time reliability in UAV-based imaging systems. Full article

The spacecraft reachable domain has become increasingly important for orbital game analysis due to growing on-orbit activities such as servicing, debris removal, and space situational awareness. This paper provides a comprehensive review of reachable domain theory and its applications in orbital games. A unified mathematical framework is established through three complementary classification dimensions: spatial attributes that distinguish absolute from relative reachable domains, temporal attributes that differentiate free-time from fixed-time reachable domains, and informational attributes that contrast deterministic and predictive reachable domains. Solution methods are systematically reviewed according to this taxonomy, covering analytical and semi-analytical methods, numerical optimization approaches, and geometric and sampling methods for spatial-scale reachable domains, as well as linearized ellipsoidal approximation, exact envelope determination, and fast analytical approximation for time-scale reachable domains. Applications are examined through three representative scenarios: one-on-one pursuit-evasion games, multi-agent cooperative games, and threat-avoidance and defense games. Key limitations of existing approaches are identified, including modeling fidelity, computational efficiency, and scalability under uncertainty. Future research directions are outlined to address these challenges. Full article

Crime surveillance in urban environments faces increasing challenges due to dynamic conditions and the demand for real-time monitoring. This paper investigates the use of video data from autonomous vehicles to enhance situational awareness in public spaces through deep learning models optimized for edge processing. High-resolution vehicle-mounted cameras serve as mobile surveillance units capable of real-time object detection, human action recognition, and anomaly detection, bridging the gap between autonomous mobility and urban monitoring. Building on this vision, we introduce ITS-Vision, a generic framework that operationalizes these use cases, enabling autonomous vehicles to function as mobile, context-aware sensing platforms. To validate this approach, we develop prototypes for key ITS-Vision components: a fight detection module using a fine-tuned X3D model, suspect identification via MediaPipe for detection combined with FaceNet for embedding extraction, and a dangerous items detection module using a fine-tuned YOLOv11n model. Due to the limited availability of real-world autonomous vehicle video datasets, experiments were conducted in controlled laboratory environments, demonstrating the feasibility of the proposed architecture and algorithms under simulated conditions. Future work will focus on collecting dedicated datasets and advancing the models toward deployment in real urban scenarios. Full article

As the number of objects in orbit increases every year, the number of objects re-entering Earth’s atmosphere grows as well. Re-entry path prediction is tricky, as atmospheric modeling lacks accuracy and requires constant monitoring of the object during re-entry. Ground-based sensors face limitations due to the field of view and weather. This paper explores the novel idea of using star trackers in very low Earth orbit to image Resident Space Objects (RSOs) that are on re-entry path and provides a comparison of different star trackers to determine the most effective parameters. A simulation with 1000 Resident Space Objects on re-entry path was performed and detectability analysis was run using AURICAM, SAGITTA, PCO, IDS, and FAI sensors placed in orbit between 200 and 600 km in altitude. The results show that all star trackers at any altitude were capable of detecting at least three RSOs on re-entry path and making at least 47 detections during the simulation period. In particular, instruments with larger aperture diameters such as SAGITTA and FAI and quantum efficiency performed better, making up to 134 detections and detecting up to 10 unique RSOs. They also detected a higher average signal-to-noise ratio. Detectability of RSOs is higher when the sensor is placed closer to the objects, with the most effective performance recorded at 300–400 km altitude. Future work should include practical testing of this technique. Full article

This paper proposes a passive in-orbit calibration method for phased array antennas using GNSS carrier-phase measurements. By performing synchronous observation and exploiting the short-baseline property between the positioning antenna and array elements, the first differencing operation eliminates space propagation errors and clock biases. By further utilizing receiver channel consistency, the second differencing operation cancels out the receiver channel errors, thereby extracting the relative receive-chain phase error of the element under test. Under typical operating conditions, the calibration accuracy can reach an RMS error of approximately $3.02\mathrm{mm}$, corresponding to a phase accuracy of $5.72°$ in the GPS L1 band. This accuracy is close to the $5.625°$ minimum phase step of a 6-bit digital phase shifter, and can be further improved under higher $C/N_0$ and well-controlled residual error conditions. Without requiring a dedicated GNSS band excitation signal, this method avoids co-frequency self-interference with the positioning antenna, which provides an auxiliary approach for in-orbit calibration of phased array receive chains. Full article

To support low Earth orbit (LEO) debris monitoring, this paper investigates a tethered-balloon-based optical observation concept intended to complement ground- and space-based sensors. The system comprises a high-altitude tethered aerostat, an optical payload, a three-axis stabilization subsystem, and a ground control station. Key payload parameters, including field of view, spatial resolution, and atmospheric transmittance, are analyzed, and the configuration is examined in terms of spectral-band selection, aperture, and multi-camera mosaic imaging. A multi-station angular-measurement model and a weighted least-squares estimator are developed for debris localization. Monte Carlo and scenario-based simulations indicate that a wide field of view can increase observation duration and availability, with mean continuous observation arcs exceeding 400 s, thereby improving estimator conditioning and localization performance. A 5 km flight experiment further validates the operability of the SWIR imaging chain through star-field imaging and a representative image-sequence example with a highlighted moving point-source target. The results suggest that tethered balloons can provide a cost-effective and rapidly deployable supplementary observation layer for multi-layer space situational awareness. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on paired pre- and post-disaster remote sensing images, which often face practical challenges in data acquisition and image pairing during emergency situations. To overcome these limitations, a hybrid swin–mamba U-shaped network (UNet) is developed for building damage assessment using only post-disaster remote sensing imagery. The proposed framework employs a Swin Transformer as the encoder to extract multi-scale features and capture long-range contextual information, while a Parallelized Patch-Aware Attention (PPA) convolution module is introduced in the decoder to restore spatial details and improve feature reconstruction. In addition, a Visual State Space (VSS) module is incorporated in the bottleneck layer to effectively model both global contextual dependencies and local structural information, thereby improving the representation of building damage characteristics from single-temporal imagery. Experiments conducted on the xBD dataset show that the proposed method outperforms the Swin–Unet by 1.7% in overall F1-score, achieving an overall F1-score of 55.2%. In addition, qualitative visualization results suggest that the proposed method has favorable generalization capability across different disaster scenarios. These results highlight the practical potential of the proposed framework for rapid post-disaster building damage assessment, particularly in emergency response scenarios where only post-disaster imagery is available. Full article

The growing sophistication of the internet and web space has spawned highly dynamic, multi-vector cyber threats that cannot be handled by automated detectives and hence the necessity to introduce analyst-oriented, cognitively powerful security analysis apparatus. The character of current visualization-based security frameworks is that they are inclined to deliver data unproactively, fail to engage the dynamic setting, and fail to comprehend the evolving motive of assailants, resulting in subsequent identification and a fractured understanding of coordinated web attacks. The paper introduces a new model of interactive security visualization known as Context-Oriented Visual Exploration of Resilient Threats (COVERT), a hybrid of behavioral context modeling, adaptive visual storytelling, and intent-sensitive interaction. COVERT is dynamically rearranged to the development of threats, patterns of interaction between analysts, and objectives of the possible attacks, which helps in releasing relevant security capabilities gradually. The framework integrates graphical threat flows, attention-directed visual cues, and real-time feedback loops to align system responses to the thinking processes of the analysts. The evaluation of high-scale web traffic and attack simulation dataset indicates that COVERT is much more effective in the multi-stage detection of attacks, false-positive interpretation is minimized, and the investigation period is reduced compared to the visualization infrastructure of the static and semi-interactive infrastructure. According to user studies, there is higher situation awareness, enhanced correlation of distributed events, and enhanced decision-making in complex web intrusion situations, such as advanced persistent threats and web exploitation coordination. Combining contextual intelligence with adaptive interaction and visualization of security, COVERT reveals that intent-based visual analytics may greatly improve internet and web threat detection and analysis systems to support more agile and resilient cyber defense procedures. The proposed COVERT strategy achieved 93% threat-detection rate, the false positives were reduced to 6%, the response time of the analysts was reduced to 140 s, and the situational awareness was increased to 88%. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

Orbit determination from optical observations remains a challenging problem due to the absence of direct range measurements and the presence of sparse, noisy, and irregularly sampled data. This work presents TRACER (Tracking, Recognition, and Analysis for Celestial Ephemerides Retrieval), a robust and fully automated framework for angles-only orbit determination. The proposed approach integrates probabilistic and deterministic strategies within a unified, decision-driven architecture. In particular, statistical ranging is employed for short-arc regimes to explore admissible solutions, while deterministic methods, including modified Gauss and Väisälä techniques, are used for longer arcs and refinement. Candidate solutions are evaluated through a unified scoring function that combines observational consistency with physically motivated penalties. A key contribution of TRACER is the introduction of a randomized subset-selection outer loop, which repeatedly solves the orbit determination problem on different observation subsets and validates solutions against the full dataset, enhancing robustness in challenging scenarios. Additional mechanisms for adaptive subarc selection, recovery from failure, and progressive data assimilation further improve reliability. The resulting framework enables fully autonomous orbit determination without manual intervention, bridging the gap between individual algorithms and operational pipelines for real-world astrometric data processing. Full article

Space-based optical detection is a critical capability for Space Situational Awareness, yet the scarcity of real on-orbit observation data significantly hampers the development and validation of object detection and tracking algorithms. To address this need, this paper proposes a high-fidelity image simulation method designed to provide reliable data sup-port for algorithm development and evaluation. The method systematically integrates or-bit propagation, high-precision astrometric corrections, imaging visibility constraints, and multi-source noise modeling. A unified Point Spread Function convolution streak model is established to consistently represent the motion blur of both stars and space objects during exposure. Additionally, simplified parametric stray light background models covering the Sun, Moon, and Earth airglow are constructed. Quantitative comparison with real image data from the Kaiyun-1 satellite demonstrates good agreement in star positions, streak morphology, and centroid localization accuracy. Preliminary validation against real data demonstrates that the proposed simulation framework can provide effective image data for testing and performance assessment of space-based situational awareness algorithms. Full article

Astronomical positioning of space targets is an important task in space situational awareness. In both ground-based and space-based optical observation scenarios, accurate positioning relies on the reliable matching of numerous stars in observational images. However, dense star scenes increase the ambiguity of local patterns and the computational burden of candidate retrieval. Building on established geometric voting and catalog-indexing strategies, this paper develops a two-stage star map matching method that specifically combines adaptive magnitude stratification with seed-guided residual-star diffusion for large-field dense star scenes. In the first stage, an adaptive magnitude-stratified bright-star subset is selected according to field density, and angular-distance voting is used to obtain reliable seed correspondences. In the second stage, residual-star candidates are retrieved from seed-centered dual-feature sub-libraries indexed by angular distance and magnitude difference, and are then refined through single-seed local diffusion and multi-seed global fusion. Experimental results from both simulated and real observational data demonstrate that the proposed method achieves a high matching success rate with low computational cost and performs effectively in large-field, dense star scenes. The proposed method provides a practical matching solution for astronomical positioning in dense star scenes. Full article

The gradual increase in man-made objects in the space surrounding our planet is becoming increasingly evident. This significant rise in terrestrial materials is reflected in a greater presence of artificial satellites, space debris and waste from space missions. These objects orbiting close to Earth pose a significant risk in the event of uncontrolled re-entry, as well as collisions between the artificial satellites themselves, launch vehicles and space stations in Earth orbit. This article presents the experimental progress achieved during the prototype phase of a new model of robotic satellite observatory (SRO), featuring significant advances in its design and capabilities. These new SROs are intended to have dual capability to operate simultaneously in both scientific and military contexts. The possibility of forming a network with these devices will provide a system that substantially improves orbital determination and the identification of space objects of interest. The result presented here is an advanced model of the SRO, featuring substantial design improvements from both an ergonomic and economic perspective, as well as a significant enhancement in its ability to monitor and track space objects of uncertain origin that may be of interest or considered a threat to security, thereby expanding its Space Situational Awareness (SSA). Full article

Raphiel Eristavi’s [Kakheti, 1824–Telavi, 1901] archival legacy constitutes a unique, underexplored corpus for examining the sociopolitical and cultural processes shaping 19th-century Georgia’s national identity. These archival documents contain his writings as a publicist, his ethnographic and geographical notes, literary texts, and private correspondence, shedding light on the intellectual and cultural dynamics of the period, particularly about reintegrating Muslim Georgian communities into the national space. Eristavi’s contributions to periodicals reflect his publicist activities, illustrating the press’s formative role in shaping public opinion, consolidating cultural identity, and fostering national awareness. His writings articulate his conviction that language, culture, tradition, and shared historical memory function as the primary instruments for reconnecting estranged territories with Georgia’s historical continuum. This entry analyzes Eristavi’s role as an intellectual and cultural mediator in integrating Muslim Georgian populations (i.e., Tao-Klarjeti and Samtskhe) into broader national frameworks, particularly in his writings on the Crimean War and Russo-Turkish War of 1877–1878, as well as how he engaged with questions about ethnic identity, territorial cohesion, and cultural memory. By situating Eristavi’s archive within the wider efforts of the Georgian intelligentsia, this study seeks to highlight his contribution to preserving language, promoting education, and reaffirming historical unity as essential components of national and state consciousness. Full article