2020 IEEE Aerospace Conference最新文献

{"title":"Instrument Data Metrics Evaluator for Tradespace Analysis of Earth Observing Constellations","authors":"V. Ravindra, S. Nag","doi":"10.1109/AERO47225.2020.9172705","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172705","url":null,"abstract":"There is currently a trend towards developing and commissioning satellite constellation missions, which has necessitated tradespace studies to design high-performance, low cost and low risk constellations. An open-source software tool called Tradespace Analysis Tool for Constellations (TAT-C) has been developed at the NASA Goddard Space Flight Center, which aims to facilitate pre-phase A mission studies by generating and optimizing the tradespace of the constellations, involving a multitude of possibly coupled parameters such as orbits, satellites, launchers, ground-stations, and instruments. The performance attributes investigated by TAT-C are instrument data metrics, coverage metrics, cost and risk. While previous research has explored optimization of the constellation satellite orbits using metrics associated with coverage (such as maximizing access duration, maximizing the number of revisits over a region), there is relatively less work on exploring the tradespace of instrument parameters and associated data metrics. Previous research has also used relatively rudimentary data-metrics such as imaged pixel resolutions, range, and angles at which observations are made. This paper describes the instrument data-metrics evaluator of TAT-C which generates more sophisticated data metrics characteristic of the instrument type. The basic concept and the architecture of the evaluator have been developed to accommodate instruments without assuming specifics about the underlying technology. In this paper, we describe the modeling of the two most common types of Earth Observing instruments, namely passive optical sensors and synthetic aperture radars (SARs). The models allow for evaluation of commonly used data-metrics such as the signal to noise ratio, noise equivalent delta temperature, ground-pixel resolutions, dynamic range, noise-equivalent sigma0, etc. The challenges in making the models generic enough for wide usage, while being able to appropriately mimic characteristics of complex real-world instruments, are described using examples such as the Landsat-8 Thermal Infrared Sensor and Operational Land Imager. Lastly, we present results from the instrument data metrics evaluator for three important use cases of mission design with passive-optical sensors and SARs. The first use case explores the tradespace of Sun-Synchronous Orbits from a perspective of data-metrics as opposed to commonly considered coverage metrics. The second use case explores the tradespace of SAR parameters and highlights quantitative trade-offs between instrument parameters influencing the performance, size, and complexity. Such tradespace analysis allows the user to appreciate and consider a fundamental constraint on the SAR antenna size depending on radar frequency. In the last use case, we explore the tradespace of pushbroom vs whiskbroom scanners, and evaluate the conditions under which their performance match.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"123 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131745526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual Clustering for Automated State Estimation by Sensor Networks","authors":"C. Diggans","doi":"10.1109/AERO47225.2020.9172792","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172792","url":null,"abstract":"Through the lens of an application in space object tracking, we develop the concept of a data-aware algorithmic similarity kernel that enables clustering of partial state observations according to their source phenomena. Particularly, we consider data sets consisting of such observations made by distributed sensor networks where pairwise comparisons yield no useful association. Utilizing the data set as context, likelihoods of association for pairs are assigned by an algorithmic similarity measure that incorporates expert knowledge and domain specific heuristics through statistical analysis of higher order tuples. Spectral clustering is applied to the resulting affinity matrix to partition the data by source, where further expert analysis can better characterize the states being observed. The example application has low dimensionality and relatively simple statistical associations that make it an ideal model for illustrating the overall approach.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"454 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116497136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an FDIR Software Fault Tree Library for Onboard Computers","authors":"S. Müller, K. Höflinger, Michal Smíšek, A. Gerndt","doi":"10.1109/AERO47225.2020.9172756","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172756","url":null,"abstract":"The increasing complexity of space missions, their software architectures, and hardware that has to meet the demands for those missions, imposes numerous new challenges for many engineering disciplines such as reliability engineering. Affected by the ever growing demand for more onboard computation power are the onboard computers. They in return require Fault Detection, Isolation, and Recovery (FDIR) architectures to support their fault tolerant operation in the harsh environment of space. Especially high performance commercial processing units face the challenge of dealing with negative radiation effects, which may significantly degrade their operation. To design performant and fault tolerant onboard computers, it is of high interest to assess the effectiveness of the FDIR architecture in the early phase of system design. This can be achieved using Fault Tree Analysis (FTA). However, to create complete fault trees manually is an error prone and labor intensive task. In this paper, the methodology for assessing the FDIR design of onboard computers in space systems, presented in [1], is refined by introducing a library of FDIR routines. The routines are modeled using fault trees and are composed into a software system fault tree using a basic fault model and a design configuration chosen by the reliability engineer. To assess the configurations, we give a heuristic based on a factor-criteria-metric model. We demonstrate the feasability of our approach on the basis of a case study on the rover of the Martian Moons eXploration (MMX) mission. Several FDIR configurations are studied and fault trees are generated for them. For the chosen case study, we obtain a reduction of up to 80% in terms of modeling effort.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128060517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Modeling of Spacecraft Safe Mode Events","authors":"Melissa Hooke, Gabriel Chandler, T. Imken","doi":"10.1109/AERO47225.2020.9172604","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172604","url":null,"abstract":"When a spacecraft experiences an unexpected anomaly that could cause permanent damage to the vehicle, the spacecraft enters a pre-specified minimally operating state called safe mode in order to protect itself from further harm. Based on data collected by the Jet Propulsion Laboratory (JPL) which spans beyond-Earth missions from the past 30 years, previous analyses have modeled the occurrence of safe mode events and the duration of their recoveries. These analyses modeled failure and recovery rates according to two Weibull probability distributions which assume independent identically distributed (iid) data across all missions and mission timelines. Model-based risk assessment teams at JPL have applied these statistical analysis directly to flight projects resulting in tangible adjustments to the mission design process, specifically for trajectory planning on future missions. In the present analysis, we argue that the iid assumption does not hold across missions. Instead, recovery times and times between safing events should be grouped and analyzed by destination rather than treated as one population. Here, this grouping is achieved through a hierarchical Bayesian architecture which prioritizes the sharing of mission data (failure and recovery times) across missions with the same destination. The hierarchical nature of the model allows for prediction of new mission safing rates without making an iid assumption. The Bayesian model is implemented using the Gibbs Sampler, a Markov Chain Monte Carlo (MCMC) technique which allows for flexible specification of distributions. An exploration of non-constant failure rates over the timeline of individual missions is also included.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132622864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Modeling Framework for the Study of Infectious Disease Spread through Commercial Air-Travel","authors":"Pierrot Derjany, S. Namilae, Dahai Liu, A. Srinivasan","doi":"10.1109/AERO47225.2020.9172285","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172285","url":null,"abstract":"This paper presents an integrated computational modelling framework combining pedestrian dynamics and infection spread models, to analyse the infectious disease spread during the different stages of air-travel. While, commercial air travel is central to the global mobility of goods and people, it has also been identified as a leading factor in the spread of several epidemic diseases including influenza, SARS and Ebola. The mixing of susceptible and infectious individuals in these high people density locations like airports involves pedestrian movement which needs to be taken into account in the modelling studies of disease dynamics. We develop a Molecular Dynamics based social force modeling approach for pedestrian dynamics and combine it with a stochastic infection dynamics model to evaluate the spread of viral infectious diseases in airplanes and airports. We apply the multiscale model for various key components of air travel and suggest strategies to reduce the number of contacts and the spread of infectious diseases. We simulate pedestrian movement during boarding and deplaning of some typical commercial airplane models and movement of people through security check areas. We found specific boarding strategies that reduce the number of contacts. Further, we find that smaller airplanes are more effective in reducing the number of contacts compared to larger airplanes. We propose certain queue configuration that reduces contacts between people and mitigate disease spread.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parker Solar Probe's 1st Year of Ka-band Operations","authors":"Matthew W. Cox, D. Copeland, R. Nikoukar, Sean Sprouse","doi":"10.1109/AERO47225.2020.9172588","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172588","url":null,"abstract":"Launched in 2018, NASA's Parker Solar Probe (PSP) mission has been described as “The Mission to Touch the Sun”. Over a 7 year prime mission, the spacecraft will make 24 close encounters with the Sun. In order to meet requirements, PSP's telecom subsystem needed to incorporate a Ka-band downlink for radiometrics and science data return. PSP uses a 0.6 meter High Gain Antenna (HGA). This antenna is used only for a Ka-band downlink, and during its use is nominally the only time the science data is transmitted. Aspects of the mission design also restrict the use of the HGA to short periods in between each solar flyby. These constraints require the RF team and Mission Operations team to be extra diligent in optimizing the downlink data return. This paper provides a summary of PSP's first year of operations at Ka-band. In addition to presenting the overall Ka-band performance, other items that will be focused on include: planning considerations for Ka-band coverage with the DSN, observed effectiveness of a HGA calibration, pre-launch link assumptions, and possible future work. With multiple future deep space missions utilizing a Ka-band downlink, this information will hopefully be beneficial to help with planning and optimizing the data return.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133537905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSL Telecom Automated Anomaly Detection","authors":"R. Mukai, Zaid J. Towfic, M. Danos, M. Shihabi, D. Bell","doi":"10.1109/AERO47225.2020.9172573","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172573","url":null,"abstract":"The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134368367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Planned Deployment of the NISAR Engineering Payload Mission Testbed","authors":"O. Sindiy, Antonette Feldman, Nicholas Zhao","doi":"10.1109/AERO47225.2020.9172380","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172380","url":null,"abstract":"The NASA-ISRO Synthetic Aperture Radar (NISAR) mission is a joint project between the National Aeronautics and Space Administration and the Indian Space Research Organisation. Once deployed, NISAR will be an Earth-orbiting dual-frequency synthetic aperture radar (SAR) observatory, which will study hazards and global environmental change. NASA will provide the mission's Engineering Payload (EP) and the L-band synthetic aperture radar (L-SAR). ISRO will provide the spacecraft bus, the S-band SAR, the launch vehicle, and associated launch services for a 2022 launch. The mission will be operated jointly. This paper describes the planned deployment of the supporting NISAR Mission Testbed (MTB) for the NASA JPL-provided EP. Specifically, this paper provides an overview of the MTB facility composition, capabilities, and known limitations; MTB team responsibilities; plans for MTB integration, checkout, and validation for certification; and the planned uses of the MTB. The planned uses of the MTB support the overarching goals of discovering any problems at the earliest possible time and to offload the System Integration and Testing (SIT) campaign for the flight hardware and software schedule relative to some of the system-level testing. Hence, selected specific testing applications include early system design and functional verification, interface verification, system-level software integration and testing, and mission system prototyping for risk mitigation.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134451303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of the Small Satellite Cost Model 2019 (SSCM19)","authors":"E. Mahr, Anh Q. Tu, Anil Gupta","doi":"10.1109/AERO47225.2020.9172374","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172374","url":null,"abstract":"Prompted by the rise in the use of small satellites throughout the space industry in the late 1980's, The Aerospace Corporation began to study small satellites to better understand the design principles that were being employed in their implementation. These studies highlighted the fact that cost models developed for traditional large satellites were not applicable to small satellites. This led to the development of the Small Satellite Cost Model (SSCM) in the mid 1990's. This model estimates subsystem- and system-level costs for satellites weighing less than 1000 kg using cost estimating relationships (CERs) derived from actual costs and technical parameters. Over the years, SSCM has evolved to account for the increasing number of small satellites that have been launched, which has included refining the CERs and increasing the scope of the model. This paper will discuss the development of the current version of SSCM released in 2019 (SSCM19). The topics covered will include the history of SSCM, the CER generation process, updates from the previous version of SSCM, the application of the model and future efforts to enhance the model.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-Based Visual Servoing Control for Spacecraft Formation Flying","authors":"L. Felicetti, J. Pomares","doi":"10.1109/AERO47225.2020.9172494","DOIUrl":"https://doi.org/10.1109/AERO47225.2020.9172494","url":null,"abstract":"This paper proposes an image-based visual-servoing algorithm that allows for optimal formation control. The proposed distributed controller utilizes visual features of other team members, retrieved from images captured by onboard cameras, to autonomously plan and perform formation acquisition, keeping or reconfiguration maneuvers. The problems of minimization of the control effort is analyzed and the paper proposes an optimal framework for developing controllers that address the issue. The viability of such a technique is explored through numerical simulations.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}