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Aerospace
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Advances in Avionics and Astrionics Systems
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Advances in Avionics and Astrionics Systems

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 10956

Special Issue Editors

Prof. Dr. Roberto Sabatini

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
Interests: aerospace vehicle design and testing; avionics and air traffic management systems; spaceflight systems design and operations; aerospace robotics and autonomous systems; guidance, navigation and control systems; Unmanned Aircraft Systems (UAS) and UAS traffic management; advanced air mobility and urban air mobility; distributed and intelligent satellite systems; space domain awareness and space traffic management; GNSS integrity monitoring and augmentation; defense C4ISR and electronic warfare systems; cognitive human-machine systems
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Gardi

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
Interests: aircraft and spacecraft modelling, simulation and optimization, digital twin and virtual prototype development, with focus on UAS/UAM and near-Earth orbits; aerospace GNC, with emphasis on multi-objective and multi-phase trajectory optimization for air and space platforms; aircraft environmental impacts modelling and assessment, aviation sustainability and greening technologies; aviation human factors engineering, human-in-the-loop testing, cognitive aerospace systems and trusted autonomy; avionics and CNS/ATM systems engineering and operations; airborne/spaceborne electro-optics and laser systems design, modelling and experimental characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Continuing rapid advances in avionics and astrionics systems are accelerating the introduction of automated decision-making functionalities and the progressive transition to trusted autonomous operations both in atmospheric and space flight. Major benefits of these capabilities include the de-crewing of flight decks and ground control centers, as well as the safe and efficient operations of air and space platforms in a shared, unsegregated environment. In the commercial aviation context, the introduction of Cyber-Physical System (CPS) architectures and Artificial Intelligence (AI) is supporting the development of single-pilot operated aircraft, with the co-pilot potentially replaced by a digital assistant and/or a remote pilot on the ground. A single remote pilot on the ground, on the other hand, will no longer be restricted to controlling a single aircraft and instead will be allowed to control multiple manned and unmanned vehicles, in line with the so-called One-to-Many (OTM) operational concept. 

Important efforts are also being devoted to the integration of Unmanned Aircraft Systems (UAS) in all classes of airspace, eliciting the introduction of UAS Traffic Management (UTM) services seamlessly integrated with the existing (and evolving) ATM framework. In particular, UTM requires substantial advances in Communication, Navigation, Surveillance (CNS) for ATM (CNS/ATM) and Avionics (CNS+A) technologies and associated regulatory frameworks, especially to enable low-altitude and Beyond-Line-of-Sight (BLoS) operations. Recent advances in communications, navigation and Sense-and-Avoid (SAA) technology are therefore progressively supporting UTM operations in medium-to-high density operational environments, including urban environments. Research efforts are also necessary to demonstrate the feasibility of CNS+A technologies capable of contributing to the emission reduction targets set by the International Civil Aviation Organization (ICAO), national governments and various large-scale international research initiatives. Therefore, growing emphasis is now being placed on environmental performance enhancements, focusing on Air Traffic Flow Management (ATFM), dynamic airspace management, 4-dimensional (4D) trajectory optimization, airport automation and, in the near future, urban flight operations. 

In addition to CNS+A technologies for air operations, space CPS and AI-based architectures are also being researched for a wide range of practical applications including commercial satellites, space transport/tourism, and interplanetary scientific missions. In this context, it is anticipated that economically viable and reliable astrionics CPS will play a fundamental role in the successful development of the space sector and significant research efforts are needed in the field of reusable space transportation systems, Space Traffic Management (STM), and Intelligent Satellite Systems (SmartSats). In particular, the operation of space launch and re-entry platforms currently requires considerable airspace segregation provisions, which if continued will become increasingly disruptive to civil air traffic. Moreover, the currently limited space situational awareness is posing significant challenges to the safety and sustainability of spaceflight due to the rapidly growing amount of resident space objects and particularly orbital debris. The deployment of network-centric CNS+A systems and their functional integration with ground-based ATM in a Space Traffic Management (STM) framework will support much more flexible and efficient use of the airspace with higher levels of safety. These evolutions will support the transition to what the research community has started designating as Multi-Domain Traffic Management. 

This Special Issue will focus on innovative works and advances in avionics and astrionics systems. Comprehensive reviews, original research articles, communications, and extended conference articles are welcome to submit for peer review and possible publication.

Prof. Dr. Roberto Sabatini
Dr. Alessandro Gardi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aerospace vehicle
  • aircraft systems
  • spacecraft systems
  • avionics
  • astrionics
  • air traffic management
  • spaceflight
  • robotics
  • autonomous systems
  • guidance, navigation and control
  • unmanned aircraft systems
  • uas traffic management
  • advanced air mobility
  • urban air mobility
  • regional air mobility
  • distributed satellite systems
  • artificial intelligence
  • machine learning
  • digital transformation
  • environmental sustainability
  • sustainable aviation
  • intelligent systems
  • satellite systems
  • distributed space systems
  • space domain awareness
  • space situation awareness
  • space traffic management
  • sensor fusion
  • satellite navigation
  • navigation systems
  • guidance systems
  • control systems
  • integrity monitoring
  • knowledge-based systems
  • safety-critical systems
  • integrity augmentation
  • defense systems
  • intelligence
  • surveillance
  • reconnaissance
  • human–machine systems

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

17 pages, 2764 KiB  
Article
Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
by Justin K. A. Henry and Ram M. Narayanan
Aerospace 2025, 12(1), 53; https://doi.org/10.3390/aerospace12010053 - 15 Jan 2025
Viewed by 439
Abstract
Major space agencies such as NASA and the ESA have long reported the growing dangers caused by resident space objects orbiting our planet. These objects continue to grow in number as satellites are imploded and space debris impacts each other, causing fragmentation. As [...] Read more.
Major space agencies such as NASA and the ESA have long reported the growing dangers caused by resident space objects orbiting our planet. These objects continue to grow in number as satellites are imploded and space debris impacts each other, causing fragmentation. As a result, significant efforts by both the public and private sectors are geared towards enhancing space domain awareness capabilities to protect future satellites and astronauts from impact by these orbiting debris. Current approaches and standards implement very large radar arrays, telescopes, and laser ranging systems to detect and track such objects. These systems are very expensive, may take significant amounts of time to develop, and are still only sparingly able to efficiently track debris targets less than 10 cm in diameter. This work proposes a theoretical passive-radar-based method using illuminators of opportunity for detecting space debris while estimating motion direction and Doppler. We show that by using a signal processing chain based on the self-mixing technique and digital filters, Doppler information can be extracted and continuously tracked by a uniform linear receiver array. This can be achieved by a passive sensor system, which has the advantage of lower cost without the need to emit signals that constrain the spectrum sharing issues. Full article
(This article belongs to the Special Issue Advances in Avionics and Astrionics Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>General 2D Bistatic Radar geometry.</p> Full article ">Figure 2
<p>Simulated BCE received signals with varied target height displacement.</p> Full article ">Figure 3
<p>Simulated STFT of the BCE received signal.</p> Full article ">Figure 4
<p>FSR Signal Processing Scheme for Doppler extraction.</p> Full article ">Figure 5
<p>(<b>a</b>) Self-mixing and FFT results with one target; (<b>b</b>) self-mixing and FFT results with two targets.</p> Full article ">Figure 6
<p>Bistatic geometry involving one target, and one receiver–transmitter pair.</p> Full article ">Figure 7
<p>FS bistatic geometry for a linear receiver array.</p> Full article ">Figure 8
<p>(<b>a</b>) Doppler shift measurements as a target passes over a single receiver; (<b>b</b>) Doppler shift measurements as a target passes over a linear receiver array.</p> Full article ">Figure 9
<p>FFT performance at selected SNR values after coherent summation.</p> Full article ">
15 pages, 1880 KiB  
Article
Analysis and Evaluation of Fault Propagation Behavior in Integrated Avionics Systems Considering Cascading Failures
by Lei Dong, Bo Peng, Xi Chen and Jiachen Liu
Aerospace 2024, 11(8), 608; https://doi.org/10.3390/aerospace11080608 - 25 Jul 2024
Viewed by 1022
Abstract
As the synthesis, modularization, and integration of avionics systems increase, the interconnections between systems and equipment within subsystems become increasingly complex, posing risks to the safety and reliability of the integrated avionics system. To address the risk of fault propagation due to functional [...] Read more.
As the synthesis, modularization, and integration of avionics systems increase, the interconnections between systems and equipment within subsystems become increasingly complex, posing risks to the safety and reliability of the integrated avionics system. To address the risk of fault propagation due to functional cascade failures in integrated avionics systems, this paper proposes a discrete dynamic fault propagation analysis method, which was applied to an all-electric braking system to assess its feasibility. First, the architectural features of the Distributed Integrated Modular Avionics system are summarized. Subsequently, the constructed system layer model is described, establishing the function–resource hierarchical architecture. Subsequently, the behavior of cascading failure propagation in discrete dynamic systems is analyzed by integrating the cascading failure analysis method from SAE ARP 4761A and considering the coupling characteristics between system properties and functions comprehensively. This approach facilitates the development of a cascading failure propagation model for DIMA based on discrete dynamic systems. Finally, by using the all-electric braking system under DIMA architecture as a case study, key Core Processing Modules and failure-prone functions are identified. The findings reveal that within this system, CPM2 and CPM6 are particularly susceptible to failure propagation, and the automatic brake function is notably vulnerable. Data show that the system’s failure rate escalates markedly after 2×104 h of operation. Performing maintenance before reaching this threshold can further mitigate risks. This practice aligns with current international aircraft maintenance time regulations. The method proposed in this paper can be applied early in the allocation of DIMA resources to enhance security and support DIMA design. Full article
(This article belongs to the Special Issue Advances in Avionics and Astrionics Systems)
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Figure 1
<p>Layered architecture of DIMA.</p> Full article ">Figure 2
<p>Steps in analyzing cascade effects.</p> Full article ">Figure 3
<p>Network topology. (<b>a</b>) Star topology; (<b>b</b>) ring topology; (<b>c</b>) bus topology; (<b>d</b>) tree topology; (<b>e</b>) mesh topology; (<b>f</b>) hybrid topology.</p> Full article ">Figure 4
<p>Topology of an all-electric brake system.</p> Full article ">Figure 5
<p>State diagram of all-electric brake system CPM1 at initial failure.</p> Full article ">Figure 6
<p>State diagram of the all-electric brake system CPM6 at initial failure.</p> Full article ">Figure 7
<p>CPM failure rate of all-electric brake system under different initial condition. (<b>a</b>) CPM1 initial fault; (<b>b</b>) CPM2 initial fault; (<b>c</b>) CPM3 initial fault; (<b>d</b>) CPM4 initial fault; (<b>e</b>) CPM5 initial fault; (<b>f</b>) CPM6 initial fault; (<b>g</b>) CPM7 initial fault; (<b>h</b>) CPM8 initial fault; (<b>i</b>) CPM9 initial fault.</p> Full article ">Figure 8
<p>Failure risk of the all-electric brake system under different initial faults.</p> Full article ">
24 pages, 7184 KiB  
Article
Trajectory Predictor and Conflict Detection Figures of Merit for a Performance-Based Adaptive Air Traffic Monitoring System
by Chen Xia, Christian Eduardo Verdonk Gallego, Adrián Fabio Bracero, Víctor Fernando Gómez Comendador and Rosa María Arnaldo Valdés
Aerospace 2024, 11(2), 155; https://doi.org/10.3390/aerospace11020155 - 15 Feb 2024
Cited by 2 | Viewed by 1795
Abstract
This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures [...] Read more.
This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time. Full article
(This article belongs to the Special Issue Advances in Avionics and Astrionics Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Example of differences between patterns of the same flow. The same <math display="inline"><semantics> <mi>γ</mi> </semantics></math> represents different geographical locations depending on the pattern. (<b>B</b>) Prediction when the flight has flown 60 NM (<math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> is 60 NM) and the target point is at 140 NM.</p> Full article ">Figure 2
<p>(<b>A</b>) An example of an instantaneous capture of the situation when <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math> enters the sector featuring candidate interacting flights (E and D) and non-interacting flights (the rest). The red triangle symbolizes <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math>, while the black ones indicate other flights; those marked with a cross are non-interacting flights. (<b>B</b>) Each red line connects a pair of points, representing a critical point. Alongside each red line, the associated predicted time of the corresponding <math display="inline"><semantics> <msubsup> <mi>γ</mi> <mrow> <mi>C</mi> <msub> <mi>P</mi> <mi>k</mi> </msub> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mi>o</mi> <mi>r</mi> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> </msubsup> </semantics></math> of each <math display="inline"><semantics> <mrow> <mi>C</mi> <msubsup> <mi>P</mi> <mrow> <mi>k</mi> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> between pattern combination [1,1] is shown. (<b>C</b>) Encounter probability between <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>f</mi> <mn>1</mn> </msub> </semantics></math> (<math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>C</mi> <mi>A</mi> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </semantics></math>), if they follow their planned trajectory.</p> Full article ">Figure 3
<p>Unsafe probability with given predicted minimum separation—draft.</p> Full article ">Figure 4
<p>Conditions to detect nuisance and missed alerts based on predicted and real time separation. The grey lines represent the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> set at 90 s.</p> Full article ">Figure 5
<p>Recurrent patterns of the two flows of analysis (pattern 1 refers to the planned route). The two flows interact in LECMPAU.</p> Full article ">Figure 6
<p>Selected arc-length of evaluation <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math>.</p> Full article ">Figure 7
<p>Residual statistics of MLP models for the flows of analysis.</p> Full article ">Figure 8
<p>Residual statistics of XGB models for the flows of analysis.</p> Full article ">Figure 9
<p>Speed variation in relation to the entry speed-test sample. The vertical lines indicate the positions of region splits in the performance heatmaps.</p> Full article ">Figure 10
<p>Comparison of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> <mrow> <msub> <mi>γ</mi> <mi>p</mi> </msub> <mo>,</mo> <msup> <mrow> <mi>C</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msup> </mrow> </msub> </semantics></math> variation by <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> of the flights between MLP and XGB models—examples 1 and 2.</p> Full article ">Figure 11
<p>(<b>A</b>) The SDR and logistic curve fit variation by <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 60 s and variation by <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> when <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> = −108 NM. The grey step line represents the ideal performance of the CD tool for the selected <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>—MLP-HTPs for F4–F24. (<b>B</b>) The same graphs using XGB-HTPs for F4–F24.</p> Full article ">Figure 12
<p>Variation of the four parameters of the sigmoid function by <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math>.</p> Full article ">Figure 13
<p>(A) STE variation by <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> when <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> = −108 NM, and STE variation by <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math> when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>t</mi> <mrow> <mi>T</mi> <mi>H</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 30 s for MLP-HTPs. (<b>B</b>) Same graphs for XGB-HTPs.</p> Full article ">Figure 14
<p>Examples of flight pairs with probability variation by <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>p</mi> </msub> </semantics></math>.</p> Full article ">Figure 15
<p>Stability of estimated encounter probability—examples.</p> Full article ">
29 pages, 9511 KiB  
Article
A Digital-Twin-Based Detection and Protection Framework for SDC-Induced Sinkhole and Grayhole Nodes in Satellite Networks
by Gongzhe Qiao, Yi Zhuang, Tong Ye and Yuan Qiao
Aerospace 2023, 10(9), 788; https://doi.org/10.3390/aerospace10090788 - 7 Sep 2023
Cited by 2 | Viewed by 1528
Abstract
In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC [...] Read more.
In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC errors in the routing program in satellite networks may lead to the emergence of Sinkhole (SH) and Grayhole (GH) nodes in the network, which may cause damage to satellite networks. To find and solve the problems in time, a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks is proposed. First, the satellite network fault model under SEU and the generation mechanism of SH and GH nodes induced by SDC errors are described. Then, the data structure based on digital twins required by the proposed detection and protection framework is designed, and the detection methods of SH and GH nodes induced by SDC errors are proposed. SKT and LLFI simulation tools are used to build a simulated Iridium satellite network and carry out fault injection experiments. Experiment results show that the accuracy of the proposed detection method is 98–100%, and the additional time cost of routing convergence caused by the proposed framework is 3.1–28.2%. Compared with existing SH and GH detection methods, the proposed methods can timely and accurately detect faults during the routing update stage. Full article
(This article belongs to the Special Issue Advances in Avionics and Astrionics Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Comparison between the method proposed in this article and existing methods. (<b>a</b>) The proposed method works at this stage, which uses DT and process files to detect occurrences of SHs or GHs. (<b>b</b>) The existing SH or GH detection method usually works at this stage, which uses node behavior and network traffic.</p> Full article ">Figure 2
<p>Satellite network fault model under SEU.</p> Full article ">Figure 3
<p>The call graph of Build_RIB.</p> Full article ">Figure 4
<p>The CFG and the instructions in the <span class="html-italic">readFile</span> function that lead to SH nodes forming.</p> Full article ">Figure 5
<p>The basic blocks and the instructions in the <span class="html-italic">writeFile</span> function that lead to SH nodes forming.</p> Full article ">Figure 6
<p>The CFG and the instructions in the <span class="html-italic">main</span> function that lead to SH nodes forming.</p> Full article ">Figure 7
<p>A typical example of a SH node caused by SEU.</p> Full article ">Figure 8
<p>The call graph of Dijkstra’s Shortest Path (DSP).</p> Full article ">Figure 9
<p>The CFG and the instructions in the <span class="html-italic">Dijkstra</span> function that lead to data loss and destination node changes.</p> Full article ">Figure 10
<p>The CFG and the instructions in the <span class="html-italic">main</span> function that lead to next-hop node changes.</p> Full article ">Figure 11
<p>The CFG and the instructions in the <span class="html-italic">Dijkstra</span> function that lead to next-hop node changes.</p> Full article ">Figure 12
<p>A typical example of a Blackhole node caused by SEU.</p> Full article ">Figure 13
<p>Topology diagram of satellite networks.</p> Full article ">Figure 14
<p>The detection and protection framework based on digital twins.</p> Full article ">Figure 15
<p>The overall workflow of the detection and protection framework.</p> Full article ">Figure 16
<p>Comparison of SH detection capabilities.</p> Full article ">Figure 17
<p>Comparison of GH detection capabilities.</p> Full article ">Figure 18
<p>The link propagation delay of IOLs and AOLs.</p> Full article ">Figure 19
<p>The distance range of a satellite–ground communication link.</p> Full article ">Figure 20
<p>Changes in file transmission delay with transmission rate.</p> Full article ">Figure 21
<p>The total routing convergence time under different situations.</p> Full article ">
19 pages, 6671 KiB  
Article
A Distributed Satellite System for Multibaseline AT-InSAR: Constellation of Formations for Maritime Domain Awareness Using Autonomous Orbit Control
by Kathiravan Thangavel, Pablo Servidia, Roberto Sabatini, Pier Marzocca, Haytham Fayek, Santiago Husain Cerruti, Martin España and Dario Spiller
Aerospace 2023, 10(2), 176; https://doi.org/10.3390/aerospace10020176 - 14 Feb 2023
Cited by 9 | Viewed by 3688
Abstract
Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the world’s waterways, among others, has grown substantially. This research work explores [...] Read more.
Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the world’s waterways, among others, has grown substantially. This research work explores the potential applicability of Synthetic Aperture Radar (SAR) and Distributed Satellite Systems (DSS) for the MDA operation. A robust multi-baseline Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) Formation Flying concept is proposed to combine several along-track baseline observations effectively for single-pass interferometry. Simulation results are presented to support the feasibility of implementing this acquisition mode with autonomous orbit control, using low-thrust actuation suitable for electric propulsion. To improve repeatability, a constellation of this formation concept is also proposed to combine the benefits of the DSS. An MDA application is considered as a hypothetical mission to be solved by this combined approach. Full article
(This article belongs to the Special Issue Advances in Avionics and Astrionics Systems)
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<p>A possible satellite system classification, with SAR satellite implementation examples.</p> Full article ">Figure 2
<p>Inter-Satellite Link (ISL) topologies within the DSS, adapted from [<a href="#B22-aerospace-10-00176" class="html-bibr">22</a>].</p> Full article ">Figure 3
<p>Chief-deputy classification (<b>a</b>) leader-follower, (<b>b</b>) pendulum, (<b>c</b>) cartwheel, (<b>d</b>) helix configuration, (<b>e</b>) same-ground track.</p> Full article ">Figure 4
<p>A simplified SAR classification. Solid lines are definitive classification and dashed line are the possible combination.</p> Full article ">Figure 5
<p>AT-InSAR baseline difference.</p> Full article ">Figure 6
<p>Example of relative eccentricity <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mrow> <msub> <munder accentunder="true"> <mi>e</mi> <mo>_</mo> </munder> <mi>i</mi> </msub> </mrow> </msub> </mrow> </semantics></math> (in red) and relative inclination <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mrow> <msub> <munder accentunder="true"> <mi>i</mi> <mo>_</mo> </munder> <mi>i</mi> </msub> </mrow> </msub> </mrow> </semantics></math> (in green) vectors of two followers <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>(<b>A</b>) Simulation of a Robust Multibaseline AT-InSAR formation for follower 1. (<b>a</b>) Shows the components of <math display="inline"><semantics> <mrow> <mi>δ</mi> <munder accentunder="true"> <mi>e</mi> <mo>_</mo> </munder> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>δ</mi> <munder accentunder="true"> <mi>i</mi> <mo>_</mo> </munder> </mrow> </semantics></math>, (<b>b</b>) shows the <math display="inline"><semantics> <mrow> <mi>δ</mi> <msubsup> <munder accentunder="true"> <mi>r</mi> <mo>_</mo> </munder> <mrow> <mi>r</mi> <mi>n</mi> </mrow> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> </mrow> </semantics></math> and (<b>c</b>) shows the along track separation between follower 1 and the chief. (<b>B</b>) Simulation of a Robust Multibaseline AT-InSAR formation for follower 2. (<b>a</b>) Shows the components of <math display="inline"><semantics> <mrow> <mi>δ</mi> <munder accentunder="true"> <mi>e</mi> <mo>_</mo> </munder> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>δ</mi> <munder accentunder="true"> <mi>i</mi> <mo>_</mo> </munder> </mrow> </semantics></math>, (<b>b</b>) shows the <math display="inline"><semantics> <mrow> <mi>δ</mi> <msubsup> <munder accentunder="true"> <mi>r</mi> <mo>_</mo> </munder> <mrow> <mi>r</mi> <mi>n</mi> </mrow> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> </mrow> </semantics></math> and (<b>c</b>) shows the along track separation between follower 2 and the chief.</p> Full article ">Figure 8
<p>Control acceleration for the follower 1—chief formation, where red is the Radial, green is the Transverse, and blue is the Normal component.</p> Full article ">Figure 9
<p>Safe condition evaluation for follower 1—chief formation. 5. Constellation of Formations.</p> Full article ">Figure 10
<p>A constellation of two formations with three satellites.</p> Full article ">Figure 11
<p>Control acceleration results with chief/follower mass ratio <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Control acceleration results with chief/follower mass ratio <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>DeltaV on a formation reconfiguration as a function of the mass ratio <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">
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