Objects In Space Research Articles

Analytical Second-Order Extended Kalman Filter for Satellite Relative Orbit Estimation

This study considers a relative orbit estimation problem wherein an observing spacecraft navigates with respect to a target space object at a large separation distance (several kilometers) using only the bearing angles obtained by a single onboard camera. Generally, the extended Kalman filter (EKF), which is based on linear relative motion equations such as the Clohessy–Wiltshire equation, is used for the relative navigation of satellites. The EKF linearizes the estimation error around the current estimate and applies the Kalman filter equations to this linearized system. However, it has been shown that nonlinearities of the orbit determination problem can make the linearization assumption insufficient to represent the actual uncertainty. Therefore, an analytical second-order extended Kalman filter (ASEKF) for relative orbit estimation is proposed in this study. The ASEKF, to sequentially estimate the relative states of satellites and their associated uncertainties, is formulated based on a second-order analytic relative-motion equation under J2-perturbtation, which can overcome the deficiencies of existing approaches that mainly focus on applications in two-body, near-circular, and linearized orbit dynamics. Numerical results show that the proposed method provides superior robustness and mean-square error performance compared to linear estimators under the conditions considered.

Distinct functional classes of CA1 hippocampal interneurons are modulated by cerebellar stimulation in a coordinated manner.

There is mounting evidence that the cerebellum impacts hippocampal functioning, but the impact of the cerebellum on hippocampal interneurons remains obscure. Using miniscopes in freely behaving male and female mice, we find optogenetic stimulation of Purkinje cells alters the calcium activity of a large percentage of CA1 interneurons. This includes both increases and decreases in activity. Remarkably, this bidirectional impact occurs in a coordinated fashion, in line with interneurons' functional properties. Specifically, CA1 interneurons activated by cerebellar stimulation are commonly locomotion-active, while those inhibited by cerebellar stimulation are commonly rest-active interneurons. We additionally find that subsets of CA1 interneurons show altered activity during object investigations. Importantly, these interneurons also show coordinated modulation by cerebellar stimulation: CA1 interneurons that are activated by cerebellar stimulation are more likely to be activated, rather than inhibited, during object investigations, while interneurons that show decreased activity during cerebellar stimulation show the opposite profile. We examined two different stimulation locations (IV/V Vermis or Simplex) and two different stimulation approaches (7Hz or a single 1s light pulse) - in all cases, the cerebellum induces similar coordinated CA1 interneuron changes congruent with an explorative state. Overall, our data show that CA1 interneurons are impacted by cerebellar manipulation in a bidirectional and coordinated fashion, and are therefore likely to play an important role in cerebello-hippocampal communication.Significance Statement Acute manipulation of the cerebellum can affect the activity of cells in CA1, and perturbing normal cerebellar functioning can affect hippocampal-dependent spatial processing, including the processing of objects in space. Despite the importance of interneurons on the local hippocampal circuit, it was unknown how cerebellar activation impacts CA1 inhibitory neurons. We find that stimulating the cerebellum robustly affects multiple populations of CA1 interneurons in a bidirectional, coordinated manner, according to their functional profiles during behavior, including locomotion and object investigations.

CLOWN: The PASO Cloud Detection for Optimization of Automatic Optical Surveys

Orbiting space objects have become in the last decade a major nuisance impacting ground astronomy and orbiting space assets, from observatories to satellites and space stations. In particular with the rise of the satellite population in Low Earth Orbits, space objects are becoming an even bigger threat and a strong problem to astronomical observations. To tackle these threats, several coordinated surveillance networks composed of dedicated sensors (telescopes, radars, and laser ranging facilities) track and survey space objects, from debris to active satellites. As part of the European Space Surveillance & Tracking network, Portugal is developing the Pampilhosa da Serra Space Observatory, with both radio and optical telescopes dedicated to the Space Situational Awareness domain, deployed at a Dark Sky destination. To optimize telescope survey time, we developed CLOud Watcher at Night (CLOWN), an application interface that automatically monitors clouds in real time. This software can correctly trace cloud positions in the sky and provide accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas. CLOWN only requires the use of an all-sky camera, which is already a norm in observatories with optical telescopes and can be used with any camera, including those for which no information about its model specification do exist. CLOWN does not require great computing power, and it does not require the installation of additional equipment. CLOWN results are very promising and confirm that the app can correctly identify clouds in a variety of different conditions and cloud types.

Generating variable length full events from partons

This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between parton and reconstructed object spaces, we employ transformers, score-based models and normalizing flows. Our method tackles the inherent complexities of the stochastic transition between these two spaces and achieves remarkably accurate results. The combination of innovative techniques and the achieved accuracy demonstrates the potential of our approach in advancing the field and opens avenues for further exploration. This research contributes to the ongoing efforts in high-energy physics and generative modeling, providing a promising direction for enhanced precision in fast detector simulation. Published by the American Physical Society 2024

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.

Expanding Understandings of Curatorial Practice Through Virtual Exhibition Building

This article reflects on the translation of gallery space into a virtually immersive experience in an era of remote access. Curators and scholars such as Mary Nooter Roberts, Susan Vogel, Carol Duncan, Tony Bennet, Stephen Greenblatt, Judith Mastai, and Barbara Kirshenblatt-Gimblett have discussed the myriad of ways in which the experience of culturally significant objects and sites in person has been critical to the study of art and its history. Focusing on theories of curation and display, I utilize practice-based examples from six virtual reality (VR) exhibitions produced in three different institutional contexts: the International Journal of Digital Art History’s online gallery, the European Cultural Center’s Performance Art program, and the Digital Humanities program at the University of California, Los Angeles. By documenting and analyzing the extended reality (XR) methods employed and the methodological approaches to the digital curatorial work, I address some of the challenges and opportunities of presenting objects in virtual space, offering comparisons to those faced when building physical exhibitions. I also consider how digital modalities provide a distinctly different paradigm for epistemologies of art and culture that offer greater contextualized understandings and can reshape exhibition documentation and the teaching of curatorial practice and museum studies.

Numerical search for the effective thermal conductivity of cracked media

Spacecraft parts accumulate damage during operation and defects that are invariably present even in new designs may grow. This leads to changes in the behavior of individual parts of the space vehicle and, consequently, to the risk of fracture. A more accurate assessment of spacecraft safety requires internal defects to be included in the material models under consideration. One of the main hazardous effects on space objects is multiple temperature heating and cooling due to periodic action of solar rays. This paper presents a study of thermal conduction of media containing cracks. It is carried out with the help of a technique developed by the authors to determine the effective thermal conductivity of materials and based on approximate numerical solution of the steady-state thermal conduction problem for a three-dimensional medium with cracks by the boundary element method. This technique allows to obtain the distribution of the temperature field and heat flux density at any point of the body under consideration, as well as to calculate the effective parameters of materials with high accuracy at relatively low calculation time using ordinary personal computers of average power. The basis of the numerical method presented in this paper is the decomposition of the desired solution into a series of some pre-calculated analytical solutions of the heat conduction equations. The dependence of the effective thermal conductivity on the density of thermally insulated cracks was considered. The formula of this dependence is proposed. Verification of the proposed methodology was carried out by comparing the numerical results of a number of problems with the results of other authors.

A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization

In large-scale many-objective optimization problems (LMaOPs), the performance of algorithms faces significant challenges as the number of objective functions and decision variables increases. The main challenges in addressing this type of problem are as follows: the large number of decision variables creates an enormous decision space that needs to be explored, leading to slow convergence; and the high-dimensional objective space presents difficulties in selecting dominant individuals within the population. To address this issue, this paper introduces an evolutionary algorithm based on population hierarchy to address LMaOPs. The algorithm employs different strategies for offspring generation at various population levels. Initially, the population is categorized into three levels by fitness value: poorly performing solutions with higher fitness (Ph), better solutions with lower fitness (Pl), and excellent individuals stored in the archive set (Pa). Subsequently, a hierarchical knowledge integration strategy (HKI) guides the evolution of individuals at different levels. Individuals in Pl generate offspring by integrating differential knowledge from Pa and Ph, while individuals in Ph generate offspring by learning prior knowledge from Pa. Finally, using a cluster-based environment selection strategy balances population diversity and convergence. Extensive experiments on LMaOPs with up to 10 objectives and 5000 decision variables validate the algorithm’s effectiveness, demonstrating superior performance.

Control system design for azimuth position of earth station antennas

Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios.

Towards Autonomous Retail Stocking and Picking: Methods Enabling Robust Vacuum-Based Robotic Manipulation in Densely Packed Environments.

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric-termed grasp shear force resilience-for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study's findings demonstrate the adaptive suction cups' ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios.

A paradigm shift in international security as a consequence of the Russia-Ukraine war

This article presents the main features of the historical genesis of the international security paradigm shift from its institutionalisation to the present day. It is shown that such genesis is sinusoidal in nature and related to the well-known 'Kondratiev waves', except that, unlike the latter, it is not economic in nature, but security in nature. In a historical and geopolitical context, five successive shifts in the international security paradigm (Pre-Systemic, Westphalian, Vienna, Versailles, Yalta-Potsdam, Unipolar) are distinguished as a normatively recognised system of international relations of war and peace, based on all countries' adherence to universally recognised principles and norms of international law, as enshrined in relevant international treaties. The main factors of the end of the era of the unipolar world and the growing role of 'fragile' states in the international security environment as potential objects of international military interventions and spaces for the deployment of local conflicts and wars were characterised. It was concluded that the war in Ukraine is a key trigger for a new paradigm shift in international security and the emergence of a new system of international relations, and that the outcome of the Russian-Ukrainian war will determine the 'starting positions' and strong arguments in the hands of the United States of America and its allies against China and its allies during the inevitable new global security conference on the creation of a new world order.

An archive-assisted multi-modal multi-objective evolutionary algorithm

The multi-modal multi-objective optimization problems (MMOPs) pertain to characteristic of the decision space that exhibit multiple sets of Pareto optimal solutions that are either identical or similar. The resolution of these problems necessitates the utilization of optimization algorithms to locate multiple Pareto sets (PSs). However, existing multi-modal multi-objective evolutionary algorithms (MMOEAs) encounter difficulties in concurrently enhancing solution quality in both decision space and objective space. In order to deal with this predicament, this paper presents an Archive-assisted Multi-modal Multi-objective Evolutionary Algorithm, called A-MMOEA. This algorithm maintains a main population and an external archive, which is leveraged to improve the fault tolerance of individual screening. To augment the quality of solutions in the archive, an archive evolution mechanism (AEM) is formulated for updating the archive and an archive output mechanism (AOM) is used to output the final solutions. Both mechanisms incorporate a comprehensive crowding distance metric that employs objective space crowding distance to facilitate the calculation of decision space crowding distance. Besides, a data screening method is employed in the AOM to alleviate the negative impact on the final results arising from undesirable individuals resulting from diversity search. Finally, in order to enable individuals to effectively escape the limitation of niches and further enhance diversity of population, a diversity search method with level-based evolution mechanism (DSMLBEM) is proposed. The proposed algorithm’s performance is evaluated through extensive experiments conducted on two distinct test sets. Final results indicate that, in comparison to other commonly used algorithms, this approach exhibits favorable performance.

Innovative methods for trackability and identification improvement of small objects for space traffic management

Technology solutions and operational implications need a evaluation for their crucial role in tracking and identification methodologies. The growth of objects in Earth orbit have made it more important that every space system can be accounted for to be able manage the collision risk between all space objects. Tracking and identification are intimately related as enhanced tracking capabilities minimizes the need for special identification techniques. For both features, the difference between active and passive systems are discussed. Tracking is primarily optical or radar; which of these that is being used drives the most operationally-relevant identification solutions. For example, radar tags can be made to be respond to the radio frequency (RF) energy from the radar and laser reflectors can be completely passive. Whereas identification aids for use by optical tracking are active light sources (of some selected frequency) requiring a power source.

Deep Neural Network closed-loop with raw data for optical resident space object detection

Abstract Optical survey is an important means for observing resident space object and space situational awareness. With the application of astronomical technique and reduction method, wide field of view telescopes have made significant contributions in discovering and identifying resident space objects. However, as the development of modern optical and electronic technology, the detection limit of instrument and infrastructure has been greatly extended, leading to an extensive number of raw images and much more sources in these images. Challenges arise when reducing this data in traditional measurement and calibration ways. Based on the amount of data, it is particularly feasible and reliable to apply machine learning algorithms. Here an end-to-end deep learning framework is developed, it is trained with apriori information on raw detections and automatic detection task is performed on new data acquired. The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope, it is demonstrated that our framework can achieve high performance compared with traditional method, and with data fusion the efficiency of system can be improved without changing hardware or deploying new devices. The technique deserves a wider application in many fields of observational astronomy.

A Multi‐Objective Evolutionary Algorithm Based on Bilayered Decomposition for Constrained Multi‐Objective Optimization

This paper proposes a multi‐objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi‐objective optimization problems. MOEA/D is an effective method for solving unconstrained multi‐objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real‐world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi‐objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state‐of‐the‐art constrained MOEA/Ds using multi‐ and many‐objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Design and tests of filtering actions for an AI-based RSOs detection and tracking algorithm

Space debris represent a huge threat for the actual and future space traffic. The continuous monitoring of the space environment around the Earth and the build-up, maintenance of Resident Space Objects (RSOs) catalogue are essential to enhance the space segment safety. In the last few years, trend towards Space Based Space Surveillance (SBSS) missions was relevant. In particular, the monitoring of space debris through on-board Star Trackers (STs) coupled with Artificial Intelligence (AI) based algorithms is being studied, modeling RSOs as clear streaks on a dark background. This work presents the advances in the development of an AI-based algorithm for streaks-like RSOs Detection & Tracking for on-board optical sensors applications like STs. The initial design of the RSOs monitoring algorithm (Mastrofini et al., 2022) is extended and tested for what concerns the tracking part with new filtering actions. The goal is the improvement of the tracking outputs quality and reliability when multiple RSOs passages occur, with different configurations. Tests both on real and simulated images from STs are shown and discussed.

What Is the Work of Animation? The Plasticity of Time in the Fourth-Dimensional Image

Plasticity is a central concept in the philosophy of Catherine Malabou and the theory of animation. And yet, the temporal dimension of transformation and mutability that Malabou foregrounds in her philosophy is missing from recent theoretical accounts of plasticity in animation studies​. ​In these accounts, plasticity appears as a mode of spatial extension, conveying the elasticity of form across a Cartesian timeline, whereas Malabou aims to understand time’s own metamorphosis and self-differentiation. This article invokes Malabou’s theorization of plasticity as the becoming of time itself rather than the becoming of things in time to rethink the perception of life-like movement in animation. Reviewing the techniques of animation that Frank Thomas and Ollie Johnston describes in The Illusion of Life as “drawing in the fourth dimension”, I argue that Malabou’s characterization of plasticity as the capacity “to see (what is) coming” ( voir venir) gets to the heart of animation as an art of subjective anticipation. Utilizing the human perceptual system’s tendency to invent new structures and relationships in response to changing inputs from the environment, animation prepares us to anticipate the unforeseeable and to perceive an order of movement typically concealed by the movement of objects in space.

Dual-space distribution metric-based evolutionary algorithm for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) are characterized by having multiple global or local Pareto solution sets. In most of multimodal multi-objective evolutionary algorithms, the distribution of population in objective space and decision space cannot be taken into account, simultaneously. In this paper, an innovative evolutionary algorithm based on dual-space distribution metric for multimodal multi-objective optimization (DsDMEA) is designed to solve this problem. Specifically, the two enhanced performance metrics are coupled together as a single dual-space distribution metric (DsDM) serving as the criterion for environmental selection. DsDMEA explores solutions on Pareto front and Pareto solutions sets that are not associated with the reference point, which show limited contributions to the distribution in dual spaces. Then DsDMEA removes any bi-noncontributing solutions using DsDM during environmental selection until the population size is met. This achieves a balance between the distribution of solutions and convergence in decision space. At the same time, this paper introduces a novel fitness computation method, enabling the precise localization of local Pareto fronts within the population. Finally, eight state-of-the-art multimodal multi-objective evolutionary algorithms are adopted to make a comparison on two types of benchmark to verify the performance of the DsDMEA. The experimental results demonstrated the DsDMEA effectiveness in solving MMOPs with local problems and traditional MMOPs.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

Intention inference for space targets using deep convolutional neural network

Intention inference for space targets is crucial for space situational awareness. This paper introduces a rapid and precise method for recognizing the intentions of non-cooperative space targets using a deep convolutional neural network (CNN). By employing a relative orbital dynamics model, an analysis of relative motion was performed, resulting in the identification of 11 distinct motion intentions for space targets. This study also describes how to generate relative motion trajectory images to create a training set for intention inference, effectively converting the problem into one of image recognition and classification. Extensive simulations were carried out to fine-tune the network hyperparameters, and the results highlight the exceptional performance of the proposed CNN-based method, which achieved an accuracy of 99.682%. This method significantly enhances recognition accuracy over other neural network-based methods for space objects and offers considerable potential for applications like spacecraft collision avoidance and strategic maneuvers among space targets.