Mission Safety Research Articles

Analysis of potential risks during the control of an unmanned underwater vehicle (UUV)

This article explores the analysis of potential risks associated with controlling unmanned underwater vehicles (UUVs), focusing on operational, environmental, and technological challenges. UUVs play a critical role in marine research, defense, and offshore industries but are subject to unique risks due to limited visibility, communication latency, and unpredictable underwater conditions. While UUVs have revolutionized underwater exploration and navigation, their reliance on advanced technologies introduces unique challenges. The study identifies the primary risk domains of UUV operations, including operational risks, human-machine interaction vulnerabilities, and environmental unpredictability. Using comprehensive scenario analysis and data from practical implementations, the article evaluates key risks such as navigation errors, system malfunctions, and communication disruptions. It further examines how artificial intelligence (AI) can be leveraged to mitigate these risks through real-time trajectory optimization, adaptive learning, and enhanced situational awareness. This research incorporates scenario-based analyses for UUVs to evaluate the most critical risk factors affecting mission safety. Furthermore, it explores how modern technologies can mitigate these risks by enhancing trajectory optimization and monitoring of UUVs during underwater navigation. By providing a holistic understanding of UUV risks, the findings underscore the need for robust control systems and operator training protocols. The research contributes actionable insights for improving UUV safety, efficiency, and reliability, laying the groundwork for further advancements in maritime robotics

Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions.

Eye-tracking analysis to assess the mental load of unmanned aerial system operators: systematic review and future directions

Abstract This article presents a systematic review on the use of eye-tracking technology to assess the mental workload of unmanned aircraft system (UAS) operators. With the increasing use of unmanned aircraft in military and civilian operations, understanding the mental workload of these operators has become essential for ensuring mission effectiveness and safety. The review covered 26 studies that explored the application of eye-tracking to capture nuances of visual attention and assess cognitive load in real-time. Traditional methods such as self-assessment questionnaires, although useful, showed limitations in terms of accuracy and objectivity, highlighting the need for advanced approaches like eye-tracking. By analysing gaze patterns in simulated environments that reproduce real challenges, it was possible to identify moments of higher mental workload, areas of concentration and sources of distraction. The review also discussed strategies for managing mental workload, including adaptive design of human-machine interfaces. The analysis of the studies revealed a growing relevance and acceptance of eye-tracking as a diagnostic and analytical tool, offering guidelines for the development of interfaces and training that dynamically respond to the cognitive needs of operators. It was concluded that eye-tracking technology can significantly contribute to the optimisation of UAS operations, enhancing both the safety and efficiency of military and civilian missions.

Single- and Multinozzle Plume Impingement to Detumble Space Debris

The management of space debris is increasingly crucial for ensuring the safety of space missions by mitigating collision risks. Most proposed debris management systems involve the capture and deorbiting of debris. However, it is also essential to eliminate any residual rotation and nutation of the debris to perform capture without accidents. The current work examines the effectiveness of thruster plume impingement for despinning space debris. A proof-of-concept study of plume impingement dynamics is conducted on a rectangular prism target using single- and multiple-nozzle arrangements for different target offsets and orientations. The three-dimensional simulation is carried out using the open-source Direct Simulation Monte Carlo code SPARTA. Staging and dynamic grid adaptation techniques are incorporated to obtain high-resolution results for the multiscale problem. It is observed that the plume–plume interaction in the multinozzle configuration leads to the beneficial formation of a relatively narrow beam of flow interacting with the target; that is, a multinozzle plume exerts a more concentrated directed force, which imparts greater countertorque across a broader spectrum of target orientations than a single-nozzle plume.

In-space cybernetical intelligence perspective on informatics, manufacturing and integrated control for the space exploration industry

The integration of cybernetic principles into space technology has led to a significant shift in spacecraft guidance systems and space station operations. This scholarly work provides a comprehensive analysis of the profound impact that cybernetics has had on space technology, particularly focusing on the development and implementation of closed-loop control systems. Building on foundational contributions to cybernetics, this paper offers a detailed analysis of the complex interplay between control mechanisms, behavioral dynamics, and information management in space informatics. The essential role of cybernetic control systems is highlighted through their critical function in enabling precise spacecraft maneuvers, as demonstrated by the guidance systems used in Apollo lunar missions during the crucial descent and landing phases. In modern space station programs, cybernetic intelligence operates much like a neural network, processing data in real-time and coordinating the various elements of the station's infrastructure. This cybernetic system excels in a wide range of tasks, from space docking maneuvers to waste management, thereby safeguarding astronaut welfare and ensuring mission success. Information serves as the cornerstone of problem analysis and processing, providing a holistic understanding of information flow within complex systems. The study concludes by highlighting the application of a cybernetically intelligent neural network in developing adaptive learning systems for nonlinear and intricate industrial processes. Ultimately, this paper underscores the transformative role of cybernetics in shaping the future of space exploration, emphasizing the seamless integration of control, information, and behavior in space technology to advance our understanding of celestial systems while enhancing the efficiency and safety of space missions.

Towards Explainable Anomaly Detection in Safety-critical Systems

Ensuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes. This paper introduces a novel methodology for the detection of systemic anomalies in the telemetry data of the International Space Station (ISS), leveraging the synergistic application of the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL). Integrated with machine learning-based normal behavior prediction model, this approach significantly enhances the explanatory of anomaly detection mechanisms. The methodology is verified and validated through its application to the thermal control system within the ISS's Japanese Experimental Module (JEM), illustrating its capacity to augment diagnostic capabilities and assist flight controllers and specialists in preserving the ISS's functional integrity. The findings underscore the importance of explainability in the machine learning-based anomaly detection of safety-critical systems and suggest a promising avenue for future explorations aimed at bolstering space system health management through improved explainability and operational efficiency.

Multi-objective performance optimization of turbofan engine for test run

Turbofan engines are widely used in military and civilian aviation fields due to their high propulsion efficiency and low fuel consumption rate, and their performance directly affects the safety and stability of flight mission. It is of great practical significance to optimize the turbine inlet temperature and high-pressure compressor speed under different thrust states, so as to improve the pass rate of the first test run. This paper proposes a multi-objective performance optimization framework for turbofan engines. On the historical production dataset of a certain type of turbofan engine, the turbine inlet temperature and high-pressure compressor speed under different thrusts are taken as target variables, and area variable a, area variable b, and angle variable c in the assembly stage are taken as attribute variables. Then, the multi-objective performance optimization model based on tree augmented naive bayes is established and compared and verified with the current mainstream algorithm for verification. Finally, combining with the posterior qualified probability inference and state combination global search method, a recommended state combination table is given to assist enterprises in the formulation of component production and manufacturing assembly standards, thereby optimizing turbofan engine performance, reducing reassembly requirements, and improving the pass rate of the first test run.

Insight into wave propagation in polyimide films and resistive grid sandwich structures towards a hybrid monitoring of hypervelocity impact

Micro-Meteoroid and Orbital Debris pose a significant threat to the safe operation of orbiting spacecraft, potentially leading to mission failure in space exploration. Quantitative characterization of hypervelocity impact (HVI) is crucial to ensure the safety and successful completion of on-orbit missions. Firstly, this study designed a three-layer sandwich structure of polyimide film with orthogonally laid resistive wires, combined with piezoelectric and resistive wire sensors, for the simultaneous acquisition of acoustic emission (AE) signals generated by HVI and measurement of perforation dimensions. Secondly, a semi-analytical finite element (SAFE) analysis of wave dispersion properties in the periodic sandwich structure is conducted with Bloch’s theorem, together with a hybrid model based on three-dimensional smoothed particle hydrodynamics and finite element methods (SPH-FEM) to comprehensively understand the AE waves and damage characteristics induced by HVI. The resulting anisotropic wave propagation characteristics with SAFE and SPH-FEM are closely matched. Thirdly, a time delay-multiplication (TDM) imaging algorithm considering wave velocity anisotropy is proposed for accurate real-time “visualization” of HVI locations. Lastly, correlations are established between projectile and perforation dimensions. The proposed algorithm for HVI multi-parameter quantification and damage detection helps evaluate the space HVI environment and HVI-induced damage to spacecraft.

A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles

Collision-avoidance path planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments presents a significant challenge, particularly considering the maneuverability limitations of fin-rudder-controlled AUVs. This study proposes a novel navigation approach that combines the strengths of Reciprocal Velocity Obstacle (RVO) principles and Model Predictive Path Integral (MPPI) control to enhance autonomous obstacle avoidance for multiple AUVs. The key innovation lies in leveraging RVO-based obstacle avoidance velocities to enhance both action sampling and cost function optimization in MPPI framework. Specifically, a neural network trained through supervised learning is utilized to convert the RVO-provided avoidance velocities into desired actions for MPPI. Additionally, these velocities are incorporated into the MPPI cost function, thereby enhancing the optimization process and facilitating convergence towards optimal, collision-free navigation solutions. Simulation experiments validated the method's capability to provide effective obstacle avoidance behavior across various complex scenarios. Results indicate that this approach successfully integrates MPPI's predictive capabilities with RVO's velocity avoidance logic, enabling AUVs to navigate efficiently through complex underwater environments while ensuring mission safety and effectiveness. This approach offers a promising new solution for autonomous navigation of AUVs, with potential applications in ocean exploration, underwater search and rescue, and other maritime operations. Future research will explore the performance of this approach in real-world underwater environments and consider the impact of additional dynamic factors.

Space debris in vicinity of collinear libration points of the Earth–Moon system

Practical space exploration has a priority the issues of safety of space missions. And an important fundamental problem arises — the study of the evolution and the stability of a space debris cloud, consisting of artificial objects remnants, or debris of extra-terrestrial origin.We discuss a new behavior of mechanical system near an unstable equilibrium position — the trajectories located in the selected neighborhood of unstable equilibrium. As an important application, we consider perturbed motion in collinear libration points vicinity of the restricted circular three-body problem.Numerical simulations for the parameters of the Earth–Moon system convincingly illustrate our theoretical study. This mathematical model allows us predicting how long the debris with certain energy can fly in vicinity of unstable collinear libration point, being close to the localized trajectory. This must be taken under consideration when planning the space missions. And also the localized trajectories may be useful when spaceship flies around the collinear libration point.

Detection of Harmful H2S Concentration Range, Health Classification, and Lifespan Prediction of CH4 Sensor Arrays in Marine Environments

Underwater methane (CH4) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of underwater CH4 detection mission, it is necessary to study the effect of hydrogen sulfide (H2S) in leaking CH4 gas on sensor performance and harmful influence, so as to evaluate the health status and life prediction of underwater CH4 sensor arrays. In the process of detecting CH4, the accuracy decreases when H2S is found in the ocean water. In this study, we proposed an explainable sorted-sparse (ESS) transformer model for concentration interval detection under industrial conditions. The time complexity was decreased to O (n logn) using an explainable sorted-sparse block. Additionally, we proposed the Ocean X generative pre-trained transformer (GPT) model to achieve the online monitoring of the health of the sensors. The ESS transformer model was embedded in the Ocean X GPT model. When the program satisfied the special instructions, it would jump between models, and the online-monitoring question-answering session would be completed. The accuracy of the online monitoring of system health is equal to that of the ESS transformer model. This Ocean-X-generated model can provide a lot of expert information about sensor array failures and electronic noses by text and speech alone. This model had an accuracy of 0.99, which was superior to related models, including transformer encoder (0.98) and convolutional neural networks (CNN) + support vector machine (SVM) (0.97). The Ocean X GPT model for offline question-and-answer tasks had a high mean accuracy (0.99), which was superior to the related models, including long short-term memory–auto encoder (LSTM–AE) (0.96) and GPT decoder (0.98).

Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.

Perturbation Detection in Space-based Human-Machine Teams (HMTs) in Different Layers

This study focuses on detecting unique and complex challenges of Human-Machine Teaming (HMT) in space missions, where coordination among humans, robots, and AI agents is critical. Such missions are beset by “perturbations”—unexpected challenges involving communication delays due to the vast distances separating team elements. These issues must be overcome to maintain the safety and effectiveness of space-based missions. This study conducted engineering tests to evaluate the impact of perturbations on communication and vehicle operations and the physiological state of the humans involved. It outlines an experimental scenario involving various space entities (e.g., lunar colonies; orbiters; rovers) to simulate space mission conditions and communication and coordination challenges. The study employs layered dynamics methods to detect perturbations in technological and cognitive team states across the mission, using sensors and information entropy as key analytical tools.

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

Development of Jets rig dedicated for an Active Launch Escape Abort System Wind Tunnel Model

The development of space launch systems requires rigorous testing and validation of safety mechanisms to ensure the protection of human life and mission-critical assets. One such safety mechanism is the Active Launch Escape/Abort System (ALEAS), designed to swiftly extract crew and spacecraft from a malfunctioning launch vehicle. To evaluate the performance of ALEAS, wind tunnel testing is indispensable. This paper presents the development of a specialized Jets Rig tailored for wind tunnel testing of an ALEAS model. The primary objectives of this research activity include the design, construction, and validation of a Jets Rig that can accurately simulate the propulsion dynamics of an ALEAS system within a wind tunnel environment. The Jets Rig incorporates a special instrumentation and control systems to replicate the complex operational conditions experienced during a launch abort scenario. By achieving this, it enables a comprehensive assessment of ALEAS performance, including thrust duration and plume interaction effects, among others. Key aspects of this study encompass the aerodynamic and structural considerations involved in designing the Jets Rig, the integration of high-fidelity sensors and data acquisition systems, and the development of advanced computational models for predictive analysis. Additionally, the research explores the challenges and solutions associated with the scalability of the Jets Rig to accommodate varying scales of ALEAS models. The findings from this project hold significant implications for advancing the safety and reliability of crewed space missions. A comprehensive understanding of ALEAS performance in a wind tunnel setting allows for the refinement of design parameters, algorithms, and the enhancement of abort system efficiency. Ultimately, the successful development of this dedicated Jets Rig contributes to the broader mission of ensuring the safe exploration of space and the protection of human life in the challenging and dynamic environment of space launch.

Martian Dust Storm Spatial‐Temporal Analysis of Tentative Landing Areas for China's Tianwen‐3 Mars Mission

AbstractChina's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation <−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen‐3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen‐3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA‐A, TLA‐C and TLA‐U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA‐A, TLA‐C and TLA‐U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T1 period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T2 period (Ls = 342‐55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T2 period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry‐descent‐landing (EDL) phase, along with one period (Ls = 45–55°) for the take‐off and ascent phase. Five circular landing zones with dust storm spatial probability <3% were selected for the Tianwen‐3 mission.

Mitigation of reentry blackout via gas injection in arc-heating facility

Communication blackouts during atmospheric reentry pose significant challenges to the safety and adaptability of spacecraft missions. This phenomenon, caused by the attenuation of electromagnetic waves by the plasma surrounding the spacecraft, disrupts communication with ground stations or orbiting satellites. Therefore, it is crucial to decrease the plasma density in the vicinity of the spacecraft to ensure an unobstructed electromagnetic wave communication path. This study proposes a methodology that involves the injection of gas from the vehicle’s wall to create an insulating layer near the surface. This thin layer maintains lower temperatures and reduced plasma density, enabling electromagnetic wave propagation without attenuation. Practical experiments were conducted in an arc-heating facility to simulate atmospheric reentry conditions. The results of the experiments provided empirical evidence of the effectiveness of the technique in mitigating communication blackout phenomena. Numerical fluid analysis within the wind tunnel chamber validated the formation of an air film layer near the experimental model owing to the injected gas. Schlieren imaging revealed distinctive jet shapes, which corroborated the findings of the numerical analysis. The wind tunnel tests that simulated atmospheric reentry environments confirmed the formation of an air film layer through gas injection, which substantiates the reduction in communication blackout. These results have the potential to improve communication reliability in space transport.

RTMS Ameliorates time-varying depression and social behaviors in stimulated space complex environment associated with VEGF signaling

Studies have indicated that medium- to long-duration spaceflight may adversely affect astronauts' emotional and social functioning. Emotion modulation can significantly impact astronauts' well-being, performance, mission safety and success. However, with the increase in flight time, the potential alterations in emotional and social performance during spaceflight and their underlying mechanisms remain to be investigated, and targeted therapeutic and preventive interventions have yet to be identified. We evaluated the changes of emotional and social functions in mice with the extension of the time in simulated space complex environment (SSCE), and simultaneously monitored changes in brain tissue of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), and inflammation-related factors. Furthermore, we assessed the regulatory role of repetitive transcranial magnetic stimulation (rTMS) in mood and socialization with the extension of the time in SSCE, as well as examining alterations of VEGF signaling in the medial prefrontal cortex (mPFC). Our findings revealed that mice exposed to SSCE for 7 days exhibited depressive-like behaviors, with these changes persisting throughout SSCE period. In addition, 14 days of rTMS treatment significantly ameliorated SSCE-induced emotional and social dysfunction, potentially through modulation of the level of VEGF signaling in mPFC. These results indicates that emotional and social disorders increase with the extension of SSCE time, and rTMS can improve the performance, which may be related to VEGF signaling. This study offers insights into potential pattern of change over time for mental health issues in astronauts. Further analysis revealed that rTMS modulates emotional and social dysfunction during SSCE exposure, with its mechanism potentially being associated with VEGF signaling.

Aerodynamic Design and Performance Analysis of Mars Ascent Vehicles

The mid-lift-to-drag ratio vehicle possesses larger dimensions and enhanced aerodynamic characteristics compared to the conventional sphere-cone reentry vehicle, suggesting its suitability for crewed missions to Mars. This research employs Computational Fluid Dynamics (CFD) simulations to investigate the influence of varying geometries on the aerodynamic properties of mid-lift-to-drag ratio vehicles. The aerodynamic analysis of four distinct configurations demonstrates that a bow shock wave forms on the windward side, leading to a reduction in flow velocity and subsequent increases in pressure and temperature after the shock wave. The simulation results for the four configurations in both laminar and turbulent flow conditions, specifically using the Shear-Stress Transport (SST) model, reveal that turbulence results in higher aerodynamic heating compared to laminar flow. Moreover, the aerodynamic heating is greater at a 40° angle of attack than at 30°. In this study, the transition phenomena of four geometric configurations are examined using the k-ω-γ transition model. The findings indicate a pronounced transition to turbulence on the windward side of the Ellipsled 2.00-0.25 and Hammerhead-Nominal configurations under conditions of high Reynolds numbers, transitioning from laminar to turbulent flow. The investigation into the aerodynamic characteristics of mid-lift-to-drag ratio vehicles holds substantial importance for ensuring the safety and success of Martian reentry missions, highlighting the critical nature of aerodynamic behavior in the context of space exploration.

Multiband photometric observations of GEO objects through Sloan filters

With the precipitous growth of the population of objects in Earth's orbit, all major space agencies and companies have made space surveillance a priority to ensure the safety of space missions. With the application of the methods of space surveillance, this article aims to characterize the population of objects in the Geostationary Earth Orbit (GEO) using optical measurements across multiple spectral bands. The study takes place in a context in which the existence of space debris poses a continuous hazard to the operations of active satellites in GEO. Photometric observations of GEO objects using Sloan filters were obtained with SCUDO, a small-aperture telescope part of the Sapienza network. Observations are divided into categories, including 11 objects: rocket bodies (4), defunct satellites (3), and active satellites (4). The spectral response of these objects has been analyzed leading to the identification of a photometric signature which was repeated for two Eutelsat satellites observed under same conditions. All the observed targets resulted in a lower magnitude in the near-infrared. Color indexes also indicate that they are redder than the Sun. This is particularly evident for SL-12 rocket bodies, whose g’-z’ reaches 1.5.