Debris Monitoring Research Articles

Dynamics modeling and simulation for satellites with optoelectronic tracking system

Abstract The optoelectronic tracking system has a strong coupling between the tracking platform and the satellite platform when rapidly capturing and precisely tracking targets. The high-precision coordinated pointing control between the two platforms is a key technology in the fields of space laser communication, debris monitoring, and astronomical observation. Firstly, a multi-level coupled dynamics model of spacecraft with two-dimensional (2D) motion platforms is derived based on the principles of momentum and angular momentum. Secondly, the coupling effects between the motion platform and the satellite platform are theoretically analyzed, highlighting the impact of platform orientation, configuration, mass properties, and motion characteristics on the coupling. Finally, a multi-level platform cooperative control strategy is proposed, and the model and control strategy are validated through mathematical simulations. The results show that the model is consistent with simulation results from ADAMS software, and the satellite attitude accuracy under the cooperative control strategy can be improved by 80%.

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.

Faint Space Target Information Extraction Based on Small Aperture Telescope in Complex Background

AbstractThere are many problems in space debris monitoring with ground‐based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two‐stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth‐detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth‐detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low‐rank matrix. We applied a certain preprocessing consisting of wavelet‐based star removal and median pre‐filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.

Citizen science supports national reporting of a Sustainable Development Goal indicator: A case study of plastic debris density on beaches

Global momentum to address plastic pollution has led to the development of national processes to monitor plastics into and within the environment. The Sustainable Development Goals (SDGs) provide guidance for reporting on plastic pollution, although compiling national estimates remains a challenge. Data availability remains a key barrier, which could be addressed through engagement with the citizen science community, which collects and maintains data at a resolution suitable for SDG reporting. Here, we demonstrate a process that uses citizen science monitoring of plastic debris on beaches to report on SDGs at the national scale. The study uses the Australian Marine Debris Initiative (AMDI) Database, which is composed of data submissions from over 1400 organisations across Australia. The AMDI was used to provide initial estimates of plastic debris density both nationally and across Natural Resource Management areas (NRMs). Before analysis, the database was filtered for data quality and alignment with guidance for SDG reporting (i.e., SDG Indicator 14.1.1b), resulting in the analysis of 3144 events across 538 sites between 2014 and 2019 (inclusive). The average density of plastic debris on beaches across Australia was between 0.155 and 0.279 counts m−2, with management relevant trends identified across NRMs. This study demonstrates the potential for the citizen science community to support SDG reporting and management processes, which could inform and enable actions to address plastic pollution.

Exploring Deep Learning for Underwater Plastic Debris Detection and Monitoring

Exploring Deep Learning for Underwater Plastic Debris Detection and Monitoring

A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine

In view of the high false alarm rate in the oil debris monitoring results of the triple-coil inductive sensor in the transmission lubrication system of the aero-engine, a new debris signal processing method based on inductive monitoring is proposed. A time domain analysis is carried out first, and the signal energy is the most effective index to distinguish the debris signature from the noise signature. On this basis, signal energy values within a fixed-length sliding window is processed through the histogram. Finally, a threshold is set for the detection of the debris signature, which is based on the distribution of data within the histogram. This method is applied to the experimental data from a test run of an aero-engine, and the results show that all the debris is detected even if part of it appears during a change in the working condition of the aero-engine. Therefore, this method shows satisfactory results in debris detection accuracy and especially the inhibition of false alarms. It is also applicable for real-time monitoring due to the similarity between the movement of the sliding window and real-time data acquisition. In addition, it is applicable for various sensing principles, including but not limited to the inductive sensor signal, since the detection performance is only related to the signal itself.

Improving the knowledge of the orbital population New technical means of space debris monitoring

Space objects monitoring data represents the foundation of any Space Traffic Management system, as it provides inputs both for operational needs (e.g. collision avoidance), but also to space debris models to assess the status and evolution of the debris environment. The current paper provides an overview of current techniques used for space object observation, ranging from radar to in-situ system, together with a survey of emerging technologies and recommendations for evolution in view of observed trends in space operations (e.g. need for improved collision avoidance data, increase in rideshare missions, development of active debris removal and servicing missions).

Transboundary Environmental Harm and the Increasing Risk of Oil Spills and Marine Debris in the Semi-Enclosed Arafura and Timor Seas Region

The Arafura and Timor Seas (ATS) region is highly productive and rich in marine resources. With the growing population and economic progress of the region, diverse sources of pollution, including nutrients, oil spills, marine debris, and pesticides, among others have become major concerns. This paper focuses on oil spills from offshore rigs and marine debris, such as plastics and derelict fishing gears, as primary regional concerns. As demonstrated by the historic Montara Wellhead Platform Incident of 2009, an oil spill seriously threatens the region. The once called Joint Petroleum Development Area is analyzed as an oil pollution hotspot in the region. Marine debris, including shoreline and ocean debris (particularly fishing-related debris), is analyzed through various reports and historic vessel traffic data. As a result, the Aru Sea is identified as a seafloor debris hotspot. The lack of scientific data on shoreline debris limits specific hotspot identification, but this study recognizes an increasing trend of marine debris beached on the shores of the ATS countries. The study results emphasize the need to ratify the International Convention on Oil Pollution Preparedness, Response and Co-operation 1990 by the ATS countries, and recommend establishing a regional platform for oil spill preparedness and creating a regional marine debris monitoring program. Collaboration with regional organizations for capacity building on marine debris and oil spill response is also suggested.

Identifying potential high-risk zones for land-derived plastic litter to marine megafauna and key habitats within the North Atlantic

The pervasive use of plastic in modern society has led to plastic litter becoming ubiquitous within the ocean. Land-based sources of plastic litter are thought to account for the majority of plastic pollution in the marine environment, with plastic bags, bottles, wrappers, food containers and cutlery among the most common items found. In the marine environment, plastic is a transboundary pollutant, with the potential to cause damage far beyond the political borders from where it originated, making the management of this global pollutant particularly complex. In this study, the risks of land-derived plastic litter (LDPL) to major groups of marine megafauna – seabirds, cetaceans, pinnipeds, elasmobranchs, turtles, sirenians, tuna and billfish – and a selection of productive and biodiverse biogenic habitats – coral reefs, mangroves, seagrass, saltmarsh and kelp beds – were analysed using a Spatial Risk Assessment approach. The approach combines metrics for vulnerability (mechanism of harm for megafauna group or habitat), hazard (plastic abundance) and exposure (distribution of group or habitat). Several potential high-risk zones (HRZs) across the North Atlantic were highlighted, including the Azores, the UK, the French and US Atlantic coasts, and the US Gulf of Mexico. Whilst much of the modelled LDPL driving risk in the UK originated from domestic sources, in other HRZs, such as the Azores archipelago and the US Gulf of Mexico, plastic originated almost exclusively from external (non-domestic) sources. LDPL from Caribbean islands - some of the largest generators of marine plastic pollution in the dataset of river plastic emissions used in the study - was noted as a significant input to HRZs across both sides of the Atlantic. These findings highlight the potential of Spatial Risk Assessment analyses to determine the location of HRZs and understand where plastic debris monitoring and management should be prioritised, enabling more efficient deployment of interventions and mitigation measures.

Through the wall human heart beat detection using single channel CW radar.

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20cm up to 100cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20-100cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects' concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring.

Perspective of non-catalogued space debris removal and monitoring

Space debris poses an increasing threat to functioning spacecraft every year, while greatest risks relate to non-catalogued space debris. Existing monitoring tools are not enough to understand situation and verify space debris models.A review of literature has shown that to date, separate monitoring tools for non-catalogued space debris are being developed, while the task of develop system is not being solved.The article presents results of forecasting small-sized space debris, which show that increase in number of non-catalogued SD exceeds growth of catalogued SD, while change in local distribution in space is less susceptible to change, due to inertia of processes.The model example shows that the solution of non-catalogued space debris active removal problem is not feasible in near future.Proposals are presented develop comprehensive monitoring system for non-catalogued space debris, consisting of ground-based monitoring facilities, remote and contact monitoring spacecraft, which together provide the maximum amount of information possible today.

How we count counts: Examining influences on detection during shoreline surveys of marine debris

Shoreline surveys are a common approach for documenting loads of marine macrodebris (≥ 2.5 cm). When surveys are conducted repeatedly over time and space, patterns in source, abundance, geographic distribution, and composition can be detected. Yet to realize their full potential, monitoring programs that rely on surveys must grapple with high variability in debris abundance, and appropriately manage uncertainty when reporting estimates of debris quantity. A potentially important source of bias in estimating debris loads from shoreline monitoring datasets is variability in debris detection rates. With this in mind, we conducted field experiments using common strip-transect marine debris survey protocols, designed to test detection of macrodebris. We quantified how protocol, shoreline, and debris characteristics influence the detectability of marine macrodebris. Detection rates varied according to debris distance from observer (0–5 m), number of observers, debris characteristics (size, color), and shoreline substrate. Our results highlight considerations for monitoring program design. Comparisons across datasets should be approached cautiously given differences in survey protocols and sources of bias that may affect debris density estimates should be quantified and addressed. We hope these results will inform marine debris monitoring efforts that are optimized for intended data use and impact.

USING REMOTE SENSING FOR PLASTIC DEBRIS MONITORING IN THE EGYPTIAN NORTHERN COAST

Abstract. Accumulation of plastic debris in water bodies has recently become a global concern, where the hotspots are being affected by a flow of toxic plastics from different sources. This study applied a science-based methodology for monitoring and assessment of macro plastic debris in the shoreline and sea surface of a pilot Egyptian Mediterranean site. Optical spectral of Sentinel-2 and Planet scope images were examined using a combination of different spectral bands with several indices and verified by the pixel-value classification method of Sentinel-2 images. Further, microwave remote sensing (Sentinel-2, SAR sensor) was analyzed to add more information to the optical study. Quantitative field measurements of marine debris were applied using UNEP/MAP metadata to support satellite results. This study found that 82% of beach accumulated materials were artificial polymers and that classification image was the most precise technique that reflects the real variety of different features. The overall accuracy of plastics detection and identification using remote sensing techniques reached about 70–80%.

Estimating the Amount of Submerged Marine Debris Based on Fishing Vessels Using Multiple Regression Model

The majority of marine debris is found in shallow waters; however, submerged debris accumulated at the sea bottom is affected by this kind of pollution. To mitigate the harmful effect of marine debris, we have to recognize its characteristics. However, it is hard to estimate the quantity of submerged marine debris because the monitoring of submerged marine debris requires greater cost and time compared to the monitoring of beach or coastal debris. In this study, we used the data for submerged marine debris surveyed in the sea near the Korean Peninsula from 2017 to 2020 and the data of fishing vessels passing through the areas from 2018 to 2020. In addition, the correlation of major factors affecting the amount of submerged marine debris was analyzed based on the fishing vessel data and the removal project data for submerged marine debris. Moreover, we estimated the amount of submerged marine debris based on the fishing vessels at the collection sites surveyed two or more times using a stepwise regression model. The average amount of submerged marine debris estimated by the model was 6.0 tonnes more than that by the removal project, for which the error was ~26.5% compared to the amount collected by the removal project. The estimation method for submerged marine debris developed in this study can provide crucial information for an effective collection project by suggesting areas that require a collection project for submerged marine debris based on the information of fishing vessels.

우주잔해물감시용 위상배열레이더의 배열모듈 개발에 관한 연구

This study examines the results of the array modules corresponding to the transceivers of phased array radars utilized for space debris monitoring. The phased array radar for space debris surveillance examined in this study utilizes the S-band radar frequency and is a digital radar system. However, the transmitter-receiver module is power-consuming and generates heat to satisfy the long distance of the radar. The array modules in this study utilized a common water-elongation path to increase the power supply efficiency such that the full digital radar can be fabricated. The measured characteristics of the single array modules of space debris radar were within an output frequency range of 2.9 to 3.35 GHz and power of at least 56 dBm in 300 µs with a pulse flatness of less than 0.5 dB in this frequency band. The maximum duty of 10% was satisfied. The noise figure of the receiver satisfied the characteristics of at most 3.21 dB.

Distribution and pollution assessment of marine debris off-shore Shandong from 2014 to 2022

This study analysed marine debris monitoring data for Shandong from 2014 to 2022 to obtain a better understanding of marine debris stocking off-shore Shandong in order to reduce marine debris pollution and improving the ecological environment of ocean. The results indicated that the abundance of coastal marine debris was 45,832 items/km2 (1118.5 kg/km2); the abundance of small/medium sized floating marine debris was 8976 items/km2 (1.38 kg/km2); and the abundance of large floating marine debris was 35 items/km2; the abundance of seafloor debris was 104 item/km2 (0.22 kg/km2). Compared with the nationwide abundance of marine debris, the quantity density of floating marine debris in Shandong was higher; the abundance of coastal marine debris and quality density of floating marine debris were lower. The majority of the Shandong marine debris was small/medium plastic, mostly from human activities. And we found no significant correlation between precipitation and the abundance of marine debris by statistical analysis.

An interview with Dr. Tim Flohrer: On low Earth orbit orbital debris mitigation, avoidance, and tracking

MIT Science Policy Review spoke with Dr. Tim Flohrer about his perspective on policy and technological aspects of Low Earth Orbit (LEO) orbital debris mitigation, avoidance, and tracking. Dr. Flohrer is the Head of the Space Debris Office at the European Space Agency (ESA). He holds a PhD in Physics and Astronomy from the University of Bern and a Master’s in Geodesy from the Dresden University of Technology. He joined the Space Debris Office as an engineer in 2007 and since 2014, he has worked for ESA’s Space Situational Awareness Programme (SSA) and Space Safety Programme, and he currently leads activities addressing the monitoring of space debris. In parallel, he also supports operational collision avoidance activities for ESA and third-party missions, re-entry predictions, mitigation analyses, long-term predictions of the space debris environment, and space debris risk assessments. Additionally, he is a delegate to the Inter-Agency Space Debris Coordination Committee (IADC). Because of his ties to ESA and deep technological knowledge, he brings a valuable, and often overlooked, international perspective on the orbital debris mitigation, avoidance, and tracking space.

Dim Staring Debris Targets Detection Method with Dense Long Trailing Star

With the gradual increase in spacecraft in orbit, space debris monitoring has become the key to the sustainable development of space missions. A staring debris detection method is proposed for high-density stars with long tails. In order to solve the problem that the gray level of a long trailing image is not stable and continuous, rectangular fitting is used to complete the aggregation of the trailing image and reduce the influence of noise on the trailing information. The occluded state of the target was analyzed, the feature calculation method was improved, the semi-occluded scene was statistically classified, the fully connected network (FCN) based finite point feature was accurately classified, and the semi-occluded image was extracted. Based on the extracted semi-occluded image, the inter-frame association can improve the success probability of target association and realize the effective detection and tracking of debris. The detection accuracy was tested for the changing inter-frame interval and signal-to-noise ratio (SNR), and the relationship between the index parameters and key parameters was given. Compared with previous literature, this design can detect and track the occluded target with a detection rate of more than 90% and a false alarm rate of less than 10%.

Ambient temperature impact on the parameters of the electrical circuit of sensing element in cluster eddy current sensor of metal chips in the lubrication systems of gas turbine engines

The detection of the wear particles in the engines lubrication systems is one of the most effective ways to assess the state of the bearing assemblies of machines and mechanisms. The most promising existing systems for online monitoring of wear particles are based on the eddy current methods for monitored parameters conversion. The reason is that such systems have the ability not only to determine the particle size, but also to recognize its magnetic properties, which makes it possible (under certain conditions) to localize the place of the defect development. At the same time, it is known that the ambient temperature changes in the monitored zone are one of the main external factors that have a significant impact on the informative parameters of eddy current converters. This is of particular importance for debris monitoring systems in high-power plants lubrication systems (e.g. gas turbine engine), where the oil temperature can vary widely (-50...+160 C). The temperature impact on the parameters of the electrical circuit of sensing element in the cluster single-coil eddy current sensor of metal chips, which is a part of the debris continuous monitoring system prototype for high-thrust aviation gas turbine engine, is studied in the article. A typical design of the single-coil sensing element is considered. The study results of the temperature effect on the sensing element inductance, as well as on the parameters of the current lead that connects the sensing element to the matching transformer and to the measuring circuit are presented. Methods for reducing the impact of temperature changes in the monitored area on the parameters of the electrical circuit of the sensing element are proposed, too.

A Multi-Contaminants Detection Sensor Based on Digital Lock-In Amplifier Module With High Sensitivity and High Detectability

Wear debris monitoring in lubrication oil has emerged as a promising solution for health monitoring and fault diagnosis of mechanical equipment. However, the debris feature signal is weak and overwhelmed in the noise. Furthermore, the traditional inductive detection sensors suffer from poor detectability to distinguish non-ferromagnetic metal particles. To address these problems, in this paper, a triple-coil inductive debris sensor combined with a digital lock-in amplifier (DLIA) module was proposed to detect and distinguish multiple contaminants in hydraulic oil so as to achieve high sensitivity and high detectability. First, we introduced the sensor design and fabrication. Second, the triple-coil inductive debris sensor model, the DLIA principle, and the performance analysis of suppressing noise for lock-in amplifier were provided, respectively. Finally, both simulation and experimental platforms were built to substantiate the effectiveness of the proposed approach. Experimental results showed that the detection limit of the designed sensor for iron, copper, and aluminium particles could be inferred as 85.4 μm, 102.4 μm, and 111.6 μm, respectively.