Space Surveillance Research Articles

Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain

BackgroundOn June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.AimTo detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.MethodsA near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf’s prospective space-time scan statistic (STSS) to detect daily emerging active clusters.ResultsAnalyses were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.ConclusionSTSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Recent developments in the implementation of European Space Surveillance & Tracking (EU SST)–Security and data policy

European Space Surveillance & Tracking (EU SST) is the European Union's operational capability for safeguarding space infrastructure and contributing to global burden-sharing in the domain of Space Situational Awareness (SSA). Implemented by a Consortium of EU member states in cooperation with the EU Satellite Centre, EU SST today serves over 100 user organizations with free services, such as Collision Avoidance for over 210 satellites. The Consortium operates a growing sensor network of radars, telescopes and lasers, which remain under the authority of the member states, reflecting the dual dimension of the SSA domain. Measurements and orbit data from the contributing sensors are shared through a dedicated platform, the EU SST Database.As the Consortium increasingly shares data through the Database on a daily basis and will be processing that data into a European catalogue precursor, the Consortium's internal Security Committee is responsible for further developing the EU SST data policy that must balance the requirements for transparency and safety of flight with security constraints linked to precise and timely information on the nature, specifications and location of certain space objects.This article reports on recent developments in the implementation of EU SST with regard to security and data policy. It highlights the unique governance and data sharing model of EU SST, the diverse architecture of existing SSA sharing agreements, and data security considerations.

Risk assessment of recent high-interest conjunctions

There have been several recent high-profile space events, notably the conjunctions of the Aeolus and Starlink spacecraft (both maneuverable) and the IRAS and GGSE-4 spacecraft (both dead). Here, we examine the timeline, activities, and risks associated with each of these conjunction events. To accomplish this, we fused observations from the Space Surveillance Network (SSN) to produce a sequence of high-accuracy orbit solutions and used that sequence to generate three-dimensional, dynamic progressions of each conjunction's miss distance, collision probability and its relation to the dilution threshold. Probability of Collision (Pc) is an essential tool for assessing conjunction threats. The Pc Topology tool characterizes the evolution of a conjunction's miss distance and collision probability, both of which are critical inputs to the maneuver avoidance decision-making process and exposing SSA deficiencies. We show the effect of combined hardbody radius (CHBR) on Pc as well as modeling with different shapes. Additionally, the Pc Topology tool reveals the calculation's placement in/out of the dilution region.The European Space Agency commanded the Aeolus spacecraft to perform an emergency maneuver to avoid this high-probability collision hazard. Our results indicate that this maneuver was warranted based upon ESA's decision threshold criteria and the Pc Topology trending analysis. While both collision events fortunately did not occur, we estimated the potential debris fields that these events may have generated had they occurred. Realizing that large debris production might be a factor in maneuver decision making, we then assess the impact of these fragmentation fields upon estimated operator workload and subsequent collision risk.

Use of two distant nesting areas as a breeding strategy of Golden Eagles (Aquila chrysaetos) in Valdres, southeast Norway

Cover photo: Golden eagle. Photo: Frode Falkenberg.
 Sixteen occupied nesting territories of Golden Eagle Aquila chrysaetos were studied between 2000 and 2020, in the southern part of the valley of Valdres, southeast Norway. Most of the study area consists of rolling hills dominated by spruce Picea abies with numerous clear-cuts. It was concluded that at least six (about 40%) of the eagle pairs in these territories move back and forth between two alternate, distant nesting areas 5.3 km apart (average). In two additional pairs, a second nesting area was considered possible. In the remaining eight, only one nesting area was found. The periods of one nesting area in use varied from 2–19 years, before moving to the other nesting area. This result was supported by panoramic mid-day surveillance of the air space between the two nesting areas during 2014–2020. The maintenance of a second core nesting area is most likely a strategy for moving to a more favourable hunting area and might be initiated by a new mate in the pair. The move might also be influenced by avoidance of a close neighbouring pair. A switch of nesting areas, as indicated by this study, could significantly affect results, when the number of eagle pairs in a certain area is counted.

African governments and the influence of corruption on the proliferation of cybercrime in Africa: wherein lies the rule of law?

ABSTRACT Recent international and domestic reports have consistently tagged Africa as a haven for cybercriminals and cybercrime perpetration. Most of these reports have attributed the absence of a holistic cybercrime legal framework and lack of implementation as the basis for their conclusion. Arguably, in the absence of a regional cybercrime legal framework to curtail cybercrime's menace in the African sub-region, African state governments have enacted legislation and policies in that regard. This paper argues that the level of corruption in government, private, and public institutions significantly influences cybercrime proliferation in Africa. This is accentuated by greed and the urge to attain greater heights in the African society by whatever means. There is evidence that despite the availability of cybersecurity measures corrupt insiders in private and public institutions relay critical information to cybercriminals to facilitate their criminal intentions. These corrupt insiders aid cybercriminals to bypass security infrastructures put in place by governments and institutions. The implication is that cybercriminals can now effortlessly victimize cybercitizens by using social networks to profile them. Suggestions for, increase surveillance of the cyber space, the better welfare package and increment of workers' emoluments, reducing political leaders' flamboyant lifestyle, amongst others are made as possible solutions.

Joint shape and centroid position tracking of a cluster of space debris by filtering on Lie groups

Space surveillance aims at detecting and tracking pieces of debris that are orbiting around the Earth. When the latter are sufficiently close to each other to form a compact cluster, they can be considered as a single extended object. State-of-the-art random-matrix methods estimate the kinematics of the object shape and centroid by assuming that it is ellipsoidal and that the sensor observations are randomly distributed within its volume. However, in accordance with the laws of orbital motion, space debris scatters taking a specific curvature. To intrinsically capture the resulting cluster shape, we propose a novel Lie-group-based parameterization of both the cluster and the sensor measurements. Then, we derive an iterated extended Kalman filter on Lie group to sequentially estimate both the centroid trajectory and the evolution of the extent parameters. Finally, numerical experiments validate the interest of the proposed method compared to a generic Gaussian-process-based extended-object tracking algorithm.

Application of attributables to the correlation of surveillance radar measurements

Space surveillance by radar is especially used for the low Earth orbit to maintain a database, also called catalogue, of objects on orbit. Among others, surveillance radars which are constantly scanning a region of interest in the sky are used for this purpose. The detections from such a radar which cannot be assigned to an already known catalogue object might not contain enough information to obtain a reliable initial orbit for a new catalogue entry from a single measured pass, also called tracklet. Instead, two tracklets can be combined to improve the quality of the initial orbit which leads to the correlation problem. This means that it has to be tested whether two tracklets belong to the same object and an initial orbit has to be derived by combining the tracklets. A common approach to condense the information in the tracklet is fitting them with so-called attributables. Because radar observations include different types of observables, the fitting of these attributables has to be considered as an important part of the entire correlation process. This paper analyses the effect of the attributable fitting considering the achieved accuracy and influence on the tracklet correlation. A new singularity-free coordinate system is introduced, which improves the results of the fitting and correlation. Finally, a test on a simulated survey scenario introduces two additional filters to remove false positive correlations. It is shown that the attributable-based approach can be applied successfully to tracklets of up to three minutes length with different detection frequencies.

Space debris cumulative flux considering the Interval Distance-based method

The availability of engineering models to estimate the risk from space debris is essential for space missions. According to current research, cumulative flux calculation is mostly carried out based on the equal-width interval discretization. The method discretizes the volume around the Earth into cells defined in earth centered inertial coordinates. The resulting debris flux onto a target object is shown to depend on the chosen size of the cells. To avoid a discretization error, this must be accounted for. In order to present reliable flux predictions for space mission, the algorithm improvement is an ongoing topic for the related research field. The aim of this study was to examine the discretization error during the cumulative flux determination process. Both the effect of interval step length and the orbital boundary are under investigation. Several typical orbits are selected as examples here and the 2018/01/03 TLE (Two Line Element) data published by the US Space Surveillance Network is used as the debris background in this paper. Furthermore, the Interval Distance-Based method for Discretization (IDD) is adopted in this paper. A position-centered flux determination method is introduced based on the IDD method. According to the example analysis, the IDD used in the flux calculation process provides results which are less affected by the interval step-size setup; and the orbital boundary has no effect on the calculation process. In other words, the discretization error is significantly reduced. The position-centered method provided a possible suggestion for the improvement of space debris environment models.

Tracklet-to-orbit association for maneuvering space objects using optimal control theory

The maintenance of a catalog of space objects is integral for Space Surveillance and Tracking (SST). In order to enable safe space operations, this catalog shall be complete, accurate and traceable. One of the challenges to keep custody of active satellites comes from their unknown maneuvers plan that adds significantly to the uncertainty on their precise dynamics. This fact can imply the failure of conventional measurement-to-orbit association methods leading at the best to an inconsistent traceability in the catalog (a physical object represented by two or more catalog objects) or, at worst, to the loss of the tracking of an object. This paper presents a method to tackle the measurement-to-orbit association in the presence of unknown maneuvers. It is based on optimal control theory coupled with the admissible region. In particular, the orbit-to-orbit association problem is solved with an energy-optimal indirect approach, which is then generalized to the measurement-to-orbit association problem using the admissible regions concept. Measurements are supposed to be a collection of time-close optical measurements or tracklet that can be represented by an attributable vector. A broad bench of simulated tests is presented in the case of a geostationary (GEO) maneuvering satellite, and the method is finally applied to a real case of a longitude-shift GEO satellite using the data provided by the TAROT telescope network.

Anonymous Access Monitoring of Indoor Areas

Video surveillance of public spaces is a feature of modern society that has expanded quite quickly and in a pervasive way during the last decades becoming a fundamental need for both individual and collective security. But, as the sophistication of this type of systems increases, the concern about threat to individuals’ right of privacy raises as well. Indeed, the video surveillance systems could breach personal privacy because location is clearly one of the most sensitive people information. Hence, preserving location privacy while achieving utility from it, is a challenging problem demanding the investigation of researchers. This paper tackles this non-trivial issue by designing a novel privacy-preserving architecture able to anonymously monitoring people access at the entrance of critical areas in an indoor space. At the same time our approach is able to provide full accountability in case of an accident or a legal requirement. Interestingly, our protocol is robust to server-side attacks and is efficient enough to be applied indoors through a set of IoT (Internet of Things) smart camera devices.

Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks

Object detection in aerial images, particularly of vehicles, is highly important in remote sensing applications including traffic management, urban planning, parking space utilization, surveillance, and search and rescue. In this article, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection. First, we propose a DNN based on YOLOv3 with various base networks, including DarkNet-53, SqueezeNet, MobileNet-v2, and DenseNet-201. We assessed the base networks and their performance in combination with YOLOv3 on efficiency, processing time, and the memory that each architecture required. In the second part, 3D depth maps were generated using pairs of aerial images and their parallax displacement. Next, a fully connected neural network (fcNN) was trained on 3D feature maps of trucks, semi-trailers and trailers. A cascade of these networks was then proposed to detect vehicles in aerial images. Upon the DNN detecting a region, coordinates and confidence levels were used to extract the corresponding 3D features. The fcNN used 3D features as the input to improve the DNN performance. The data set used in this work was acquired from numerous flights of an unmanned aerial vehicle (UAV) across two industrial harbors over two years. The experimental results show that 3D features improved the precision of DNNs from 88.23 % to 96.43 % and from 97.10 % to 100 % when using DNN confidence thresholds of 0.01 and 0.05, respectively. Accordingly, the proposed system was able to successfully remove 72.22 % to 100 % of false positives from the DNN outputs. These results indicate the importance of 3D features utilization to improve object detection in aerial images for future research.

Mathematic-systemic researching with the involvement of fractals when processing of space surveillance systems on recognition of waste disposal objects

The article analyzes the factors that stimulate research and development of an automated system for processing and analyzing satellite imagery data. In particular, the study of satellite imagery for the presence of waste disposal objects is considered as an application. This problem is of particular interest when conducting scientific research. The development of automated control systems (ACS) in the field of space image (SI) recognition is characterized primarily by the approach of interest, i.e. for what purpose and for solving of which particular problem of the subject area the system is created. Obviously, the system is a set of certain information technologies and the main factor in the development of an automated system is and remains the study of peculiarities of creating an appropriate information system (IS). The article proposes a methodological approach to the processing and analysis of data by the IS. The approach is based on the conceptual idea of fractal sets. This enhances the detection of abnormal signals in difficult phono-target environments. Testing is conducted on the detection of WDS under conditions of a low signal-to-noise ratio.

Protests and Public Space Surveillance: From Metadata Tracking to Facial Recognition Technologies

Protest movements are gaining momentum across the world, with Extinction Rebellion, ‘Black Lives Matter, and strong pro-democracy protests in Chile and Hong Kong taking centre stage. Yet many governments have implemented various measures that severely impair the right to protest, especially during crises, like the global COVID-19 pandemic. In this submission to the Thematic Report to the 50th Session of the Human Rights Council, I draw attention to several issues: • The role of metadata tracking and data retention laws in suppressing political protests around the world; • The chilling effect of facial recognition technologies used in public spaces on the right to peaceful assembly and protest, and the absence of oversight mechanisms or adequate safeguards in many countries around the world; • The role of private actors in assisting governments to curtail protests; and the insufficiency of the international human rights legal framework to ensure the protection of human rights by private actors.

Comparative analysis of global optical observability of satellites in LEO

With a growing number of resident space objects (RSOs), the facilities for near-Earth space surveillance have to cope with increasing workload. It also applies to low-cost small optical surveillance facilities which may present regional, national and global networks. Improved methods of planning and scheduling optical telescopes are required to use these instruments efficiently. Today, optical observations are only feasible if the following quite stringent requirements are met: the object should be illuminated by sunlight, and it should be above while the Sun is below the observer’s horizon. For different orbits, these preconditions result in varying degrees of the space object observability at various ground-based sites. Certainly, satellites in low Earth orbit (LEO) are particularly difficult to observe. This study aims at developing a new technique for assessing observability of a satellite in different types of orbits – namely, low, medium and high Earth orbits, imaging of the opportunity for its visibility in respective diagrams and their analysing for the existing near-Earth population of RSOs. Unlike other researches, wherein one or several observational stations have been chosen as target sites for in-depth analyses of visibility of all the satellites or just the selected ones, the present study focuses on examining the probability of optical surveillance of satellites in a certain orbit from any locations worldwide. It offers considerable scope for automation of surveillance planning and scheduling optical surveillance networks.

LEO Object’s Light-Curve Acquisition System and Their Inversion for Attitude Reconstruction

In recent years, the increase in space activities has brought the space debris issue to the top of the list of all space agencies. The fact of there being uncontrolled objects is a problem both for the operational satellites in orbit (avoiding collisions) and for the safety of people on the ground (re-entry objects). Optical systems provide valuable assistance in identifying and monitoring such objects. The Sapienza Space System and Space Surveillance (S5Lab) has been working in this field for years, being able to take advantage of a network of telescopes spread over different continents. This article is focused on the re-entry phase of the object; indeed, the knowledge of the state of the object, in terms of position, velocity, and attitude during the descent, is crucial in order to predict as accurately as possible the impact point on the ground. A procedure to retrieve the light curves of orbiting objects by means of optical data will be shown and a method to obtain the attitude determination from their inversion based on a stochastic optimization (genetic algorithm) will be proposed.

Visible Thermal Person Reidentification via Mutual Learning Convolutional Neural Network in 6G-Enabled Visual Internet of Things

Visible thermal person reidentification (VT Re-ID) in the 6G-enabled Visual Internet of Things (VIoT) is essential for implementing 24-h surveillance in wide-area space. The existing methods mainly focus on reducing the visual difference between visible and thermal visual streams (V-streams). However, they cannot fully exploit the information of visible and thermal V-streams, which leads to suboptimal representations. In this article, we propose a novel deep network termed the mutual learning convolutional neural network (MLCNN) to transfer useful information between visible and thermal V-streams for VT Re-ID in 6G-enabled VIoT. The proposed MLCNN consists of the feature generation module and the mutual learning module. The feature generation module aims to map the input samples into a more discriminative feature space in order to decrease the intramodality difference. The mutual learning module employs the identification loss to supervise the predictive identity accuracy and utilizes the Kullback–Leibler divergence to constitute the mutual learning loss for useful information transfer. Extensive experiments on two popular visible thermal data sets (SYSU-MM01 and RegDB) prove the effectiveness of the proposed MLCNN.

Stray light nonuniform background correction for a wide-field surveillance system.

Wide field and a long exposure time can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, such systems are very susceptible to stray light. A stray light nonuniform background will cause great interference to subsequent target recognition, resulting in a large number of false alarms. This study presents an accurate and robust correction algorithm, called the improved new top-hat transformation (INTHT), for a stray light nonuniform background. First, we analyzed the formation mechanism and influence of the stray light nonuniform background. Then, to retrieve the lost targets, the size relationship of the two different, but related, structural operators is changed so the sizes of two structural operators are not equal. Finally, before comparing to the original image to take the minimum value, we added an expansion operation to restore the background size transformation caused by the different sizes of the structural operators in the previous step. This will ensure that there is no residual stray light nonuniform background. We believe, to the best of our knowledge, that the experimental results for the real captured image datasets demonstrate that, compared to other algorithms, the proposed INTHT algorithm has a higher accuracy and effectiveness in correcting a stray light nonuniform background.

Reimagining Crises in the Indian University

The public university in India was conceptualized to provide economically accessible and socially inclusive education. It has had the dual task of simultaneously critiquing and serving the nation. However, along with the declining quality of higher education, there has also been a significant decline in public funding recently. Increasing government intervention in the everyday life of university has further resulted in policies that inadvertently have much to do with increasing surveillance and regulation of university spaces. Placing this within the ambit of the national political situation, there was a lot of student activism and unrest on city streets and inside campuses. A global public health crisis in the midst of all this resulted in a tangential change from the above issues to those of infrastructure, access, and inclusion. At this juncture then, it becomes imperative to reevaluate the purpose of the existing institutions. The paper thus is an attempt to contextualize contemporary Indian higher education, in times of the pandemic. A qualitative approach using unstructured (physical and virtual) conversations along with literature survey has been used. An analysis of the factors, like research quality, institutionalized discrimination, infrastructure, and public funding, is used to decipher how the public university in India negotiates and exists as a prominent social institution in the country.

Fluid Pandemic: Disease Surveillance, Phophylactic Mobility, and Privatization of Air Space in the Covid-19 Outbreak

Fluid Pandemic: Disease Surveillance, Phophylactic Mobility, and Privatization of Air Space in the Covid-19 Outbreak

On-Orbit Observations of Conjuncting Space Objects Prior to the Time of Closest Approach

Conjunction assessment of space objects in Low Earth Orbit (LEO) generally uses information collected by ground-based space surveillance sensors. These sensors track both the primary object (normally an active satellite) and the secondary object (typically space debris). The tracking data is used to update both objects’ orbits for collision risk assessment. The primary satellite’s involvement in this process is that of a satellite in jeopardy - the primary satellite does not usually contribute tracking data on the secondary as they are typically unequipped to do so. In this paper, an examination how an at-risk LEO primary satellite could obtain optical tracking data on a secondary object prior to the Time of Closest Approach (TCA) and assess its own collision risk without the need for additional ground-based space surveillance data is performed. This analysis was made possible by using in-situ optical measurements of space objects conjuncting with the Canadian NEOSSat Space Situational Awareness R&D microsatellite. By taking advantage of the near “constant-bearing, decreasing range” observing geometry formed during a LEO conjunction, NEOSSat can collect astrometric and photometric measurements of the secondary object in the time prior to TCA, or in the multiple half-orbits preceding TCA. This paper begins by describing the in-situ phenomenology of optically observed conjunctions in terms of the observing approach, geometry and detected astrometric and photometric characteristics. It was found that conjuncting objects are detectable to magnitude 16 and astrometric observations can be used for position covariances in the computation of probability of collision. Illustrative examples are provided. In orbits prior to TCA, in-track positioning error is improved by a factor of two or more by processing space-based observations on a filtered position estimate of the secondary. However, cross-track positioning knowledge is negligibly improved due to the inherent astrometric measurement precision of the NEOSSat sensor and the oblique observing geometry during conjunction observations. A short analysis of object detectability where star trackers could be used to perform similar observations finds that larger payload-sized objects would generally be detectable. However, smaller debris objects would require higher sensitivity from the star tracker if employed for optical conjunction derisk observations.