Abstracts:The increased risk of collisions between objects orbiting the earth is a topic of great relevance in the framework of space traffic management. Resident space objects are very difficult to detect and identify in terms of orbital parameters due to their variable sizes and the too-short duration of available observations in most cases (too-short arcs problem). An initial orbit determination algorithm, which employs a combination of metaheuristic and physics-informed frameworks as backbone and integrates an adaptive filtering module to improve the robustness to random parameter initialization and outliers, has been developed and described in this work. The algorithm has been tested on a created dataset of 1800 simulated mission scenarios with random targets and space observers, considering variable observation duration from 20 s to 60 s. Results show that in 99.61% of the scenarios, the reference orbit is retrieved with an accuracy on the final target's state greater than 1 km and 0.001 km/s and a mean root-sum-squared error across the observation arc of 0.002 km.

Abstracts:With the increase in global positioning service demands and the requirement for more precise positioning, assisting existing medium- and high-orbit satellite-enabled positioning systems with low Earth orbit (LEO) satellites has garnered widespread attention. However, providing low computational complexity performance analysis for hybrid LEO/medium Earth orbit (MEO) massive satellite constellations remains a challenge. In this article, we introduce for the first time the application of stochastic geometry (SG) framework in satellite-enabled positioning performance analysis and provide an analytical expression for the $K$-availiability probability and $K$-localizability probability under bidirectional beam alignment transmissions. The $K$-localizability probability, defined as the probability that at least $K$ satellites can participate in the positioning process, serves as a prerequisite for positioning. Since the modeling of MEO satellite constellations within the SG framework has not yet been studied, we integrate the advantages of Cox point processes and binomial point processes, proposing a doubly stochastic binomial point process for accurate modeling of MEO satellite constellations. Finally, we investigate the impact of constellation configurations and antenna patterns on the localizability performance of LEO, MEO, and hybrid MEO/LEO constellations. We also demonstrate the network performance gains brought to MEO positioning systems by incorporating assistance from LEO satellites.

Abstracts:This article investigates the observer-based composite adaptive fuzzy fixed-time consensus control issue for multiple spacecraft attitude system with unmeasurable states. Fuzzy logic systems (FLSs) are applied to tackle unknown nonlinearity in spacecraft system. Based on FLSs, a fuzzy observer is constructed for the estimation of unmeasurable states. Meanwhile, to improve the approximation performance of FLSs, a fixed-time serial-parallel estimation model is designed. The quadratic function is employed to address the potential singularity issue in the derivative of the virtual controller. In addition, a filter with fixed-time stable is introduced to avert the complex explosion issue. To this end, a fixed-time composite adaptive consensus control scheme is designed by considering the prediction error of the serial-parallel estimation model. Under the proposed scheme, the boundedness of all signals in the closed-loop system is ensured and the tracking error converges to a small neighborhood of zero within a fixed time. Eventually, the feasibility of the proposed control scheme is illustrated by the simulation results.

Abstracts:The integration of uncrewed aerial vehicles (UAVs) with vertical takeoff and landing capability into commercial and military applications marks a significant advancement in aerial technology, necessitating robust systems for safe operation. Performing autonomous safe landing in emergencies remains a critical concern among other challenges. Emergencies in UAVs can arise from various factors, such as system failures, adverse weather conditions, or mission-critical situations requiring immediate landing. Addressing this challenge, this article presents an autonomous safe landing system designed to provide operation time assurance for UAVs flying over unknown terrains. To achieve this, we partition the point cloud data generated by the LIDAR sensor into grids for safe landing site identification and selection. Our approach requires a low computational load and is validated through tests on various terrain types under real-world conditions while utilizing only the onboard sensing and computational capability of the UAV.

Abstracts:Infrared small target detection (IRSTD) methods have been extensively investigated within the infrared search and tracking applications. U-shaped networks and their improved versions have significantly enhanced IRSTD segmentation performance in recent years. However, existing methods overlook the feature dilution and insufficient representation of long-range dependencies in feature maps, hindering the accurate segmentation of small targets obscured by background clutter. To handle this problem, we designed a multiscale feature fusion and nested structure-based high-order UNet (MNHU). Our approach utilizes a high-order UNet paradigm that progressively calibrates infrared feature maps, and integrates hierarchical features to enhance both small target and background texture extraction performance. High-order U-Net selectively integrates feature maps from highly correlated adjacent nodes. Subsequently, these encoding or decoding feature maps are progressively merged into a high-order fusion feature map, ensuring sufficient feature extraction and precise feature representation. We evaluate the proposed high-order UNet-based methods (MNHU-E, MNHU-D, and MNHU) on three public datasets. The results underscore our method's superior performance in target enhancement and texture awareness, outperforming state-of-the-art techniques in intersection over union, Dice similarity coefficient, precision, and sensitivity. MNHU exhibits robust segmentation capabilities and generalization, effectively achieving a balance between model complexity and performance, which highlights its suitability for practical applications.

Abstracts:Radar signal sorting is one of the crucial techniques in radar reconnaissance. However, as the electromagnetic environment increasingly complex and the density of radar pulses surges, the efficiency of clustering-based sorting algorithms is severely degraded. To better align with the streaming data characteristics of radar pulses and avoid the storage and computation of large amounts of pulse data, this article proposes a rapid radar signal sorting method based on density and Gaussian mixture model data stream clustering. First, by introducing the concepts of core clusters and outlier clusters based on a Gaussian mixture model (GMM), the proposed method achieves effective storage of online summary information for radar emitter signals with reduced space complexity. Meanwhile, by optimizing strategies for cluster evolution and incremental updates during the online phase, and by directly outputting clustering results using GMM in the offline phase, the approach achieves a significant reduction in computational load and further enhances overall efficiency. Experimental simulation results demonstrate that the proposed method excels in terms of efficiency, providing a practically valuable method for sorting radar pulse streams.

Abstracts:Deep space exploration missions face significant environmental uncertainties, while navigation and guidance systems suffer from high latency due to their dependence on ground-based infrastructure. Therefore, the development of autonomous deep space exploration technologies is crucial for advancing future missions. Building on this foundation, this article focuses on vision-based hovering control in close proximity to an asteroid within deep space environments. We begin by constructing an image dynamics model based on the image Jacobian matrix of feature points, followed by a controllability analysis using the controllability matrix. Subsequently, an image-based extended Kalman filter is developed to estimate the lander's current velocity state. Leveraging this foundation, a 6-DOF controller is designed using the backstepping method, enabling autonomous, fixed-point hovering in close proximity to an asteroid. Finally, the effectiveness and superiority of the proposed method are validated through simulations and comparative analysis.

Abstracts:Multifunctional radar (MFR) can adjust to different working modes based on surrounding environment information, providing a degree of antijamming capability. With the enhancement of information exchange capabilities, technologies such as cooperative detection and guidance have advanced significantly, further strengthening the antijamming capabilities of MFR network. Therefore, this article builds a networked radar (NR) model based on complex network theory and presents the radar countermeasure game problem of joint jamming type selection and power allocation under power constraints. Specifically, this article employs multiagent reinforcement learning algorithms to optimize the selection of jamming types and power allocation for multiple jamming beams, thereby maximizing the long-term jamming effectiveness on NR. To address the hybrid action space decision-making problem in jamming, a serial hierarchical policy network with a shared evaluation network is designed. In addition, according to the jamming task, the state characteristics and total reward are designed reasonably to assist the strategy exploration. Finally, we propose an improved multiagent hybrid proximal policy optimization (IMAHPPO) algorithm for devising jamming strategies and compare its performance with the standard MAHPPO algorithm and the multiagent advantage actor-critic algorithm, demonstrating the effectiveness and adaptability of the proposed approach.

Abstracts:The recognition of synthetic aperture radar (SAR) jamming has gained significant attention due to its critical role in enhancing radar system performance in complex electromagnetic environments. However, existing research typically addresses a limited number of jamming categories, hindering practical applications that require the simultaneous identification of multiple jamming sources. Moreover, the challenges in feature distribution optimization lead to overlaps in interclass features and inconsistencies within intraclass features, leading to a decrease in the accuracy of SAR jamming recognition. To tackle these issues, we propose a time–frequency-aware hierarchical feature optimization method for SAR jamming recognition. Specifically, we propose a time–frequency-aware dual attention mechanism to focus on crucial features in the time–frequency map of the SAR jamming signal through parallel attention and unified attention. In addition, we propose a hierarchical jamming feature distribution optimization method that further improves the compactness and discrimination of SAR jamming feature representation. We construct an SAR jamming signal dataset with 50 jamming types to validate the proposed recognition method. The detailed results of ablative experiments and comparative experiments demonstrate the superior performance across different jamming-to-noise ratio conditions achieved by our method, establishing its efficacy for robust SAR jamming recognition.

Abstracts:The increasing focus on lunar exploration requires innovative power solutions to support scientific research, mining, and habitation in the Moon's extreme environment. Optical power beaming (OPB) has emerged as a promising alternative to conventional systems. However, the impact of lofted lunar dust (LLD) on optical transmissions remains poorly understood. This research addresses that gap by evaluating LLD-induced attenuation and optimizing OPB design for efficient power delivery over long distances. A combined theoretical and simulation-based approach is employed, utilizing the T-matrix method to model LLD attenuation and Gaussian beam theory to optimize transmission and receiver parameters. The results indicate that LLD significantly attenuates ground-to-ground optical power transmission in illuminated regions, thus making OPB more suitable in darker areas, such as permanently shadowed regions or during the lunar night. Furthermore, we demonstrate that OPB can operate over long distances on the Moon while maintaining reasonable aperture sizes through appropriate optical design optimizations. These findings highlight the potential of OPB as a reliable power solution for sustainable lunar exploration and habitation.