Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943894
A. Niklas, M. Sambora
The WorldView-3 and Landsat-8 satellites are the most recently deployed systems in their constellations and the unique data from these sensors can positively impact environmental and military target detection applications. The research team uses spectral library data in the VNIR and SWIR spectral bands of WorldView-3 and Landsat-8 to determine the best combination of spectral bands and spectral distance measure to yield the largest spectral distance value for each target material. Spectral distance measures include Euclidean Distance, Spectral Angle Mapper, Spectral Correlation Measure, and Spectral Information Divergence. The optimal configuration results are stored in a look-up-table for implementation in an automated target detection system. The Freedman-Diaconis and Shimazaki-Shinomoto methods for optimal histogram bin width determination are applied to spectral distance measures that are cross computed for each material in the spectral library and for each sensor. The bin width determination is used to characterize material clusters based on intercluster and intracluster spectral distances. The material cluster characterization results are stored in a look-up-table for fast histogram based initialization of clustering algorithms. The research team uses the in-band spectral library data for determining end member abundance estimates based on combinations of spectral bands, end member combinations, spectral distance measure, and additive white Gaussian noise for both sensors. The endmember abundance estimates are optimized using Differential Evolution, Least Squares, and Linear Simplex. The numerical accuracy of the end member abundance determination is compared across the three optimization algorithms. The completion of this foundational work increases the data exploitation potential of WorldView-3 and Landsat-8 by providing a fundamental characterization of material separability with respect to these sensors.
{"title":"Spectral library material separability using WorldView-3 and Landsat-8 spectral bands","authors":"A. Niklas, M. Sambora","doi":"10.1109/AERO.2017.7943894","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943894","url":null,"abstract":"The WorldView-3 and Landsat-8 satellites are the most recently deployed systems in their constellations and the unique data from these sensors can positively impact environmental and military target detection applications. The research team uses spectral library data in the VNIR and SWIR spectral bands of WorldView-3 and Landsat-8 to determine the best combination of spectral bands and spectral distance measure to yield the largest spectral distance value for each target material. Spectral distance measures include Euclidean Distance, Spectral Angle Mapper, Spectral Correlation Measure, and Spectral Information Divergence. The optimal configuration results are stored in a look-up-table for implementation in an automated target detection system. The Freedman-Diaconis and Shimazaki-Shinomoto methods for optimal histogram bin width determination are applied to spectral distance measures that are cross computed for each material in the spectral library and for each sensor. The bin width determination is used to characterize material clusters based on intercluster and intracluster spectral distances. The material cluster characterization results are stored in a look-up-table for fast histogram based initialization of clustering algorithms. The research team uses the in-band spectral library data for determining end member abundance estimates based on combinations of spectral bands, end member combinations, spectral distance measure, and additive white Gaussian noise for both sensors. The endmember abundance estimates are optimized using Differential Evolution, Least Squares, and Linear Simplex. The numerical accuracy of the end member abundance determination is compared across the three optimization algorithms. The completion of this foundational work increases the data exploitation potential of WorldView-3 and Landsat-8 by providing a fundamental characterization of material separability with respect to these sensors.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124880477","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943877
N. Vandermey, William M. Heventhal, T. Ray
The Cassini F-Ring & Proximal Orbits (FRPO) is a new and unique mission; to ensure the highest priority science gets implemented, the POST (Proximal Orbit Science Team) was created to pre-allocate the time around periapse for all 22 proximal orbits. The F-ring orbits, and proximal time outside of POST, were handled similar to Cassini's Solstice Mission using the Pre-Integrated Event (PIE) process. The new and unique properties of the spacecraft's trajectory required much forethought to be flown safely while still planning for the most and best science return possible. Some ring-plane crossings (RPX) will be protected against dust impacts by turning the high gain antenna (HGA) to the dust RAM direction (HGA2RAM). If on the first proximal RPX higher than expected dust readings are seen then the Project Office may choose to require more (all) subsequent RPX to be HGA2RAM, implemented via a real-time command overlay for uplinked sequences. The pointing uncertainties will be larger than usual after the final targeted flyby; some of the process changes to address this include adding extra orbit trim maneuvers (OTMs) (fuel permitting) to resync to the reference trajectory and reduce pointing uncertainties; and movable blocks of commands to be used for some periapses where atmospheric drag may cause large timing shifts Changes made for FRPO to address perceptions that these sequences will be hard to implement include requiring early pointing designs (during integration) for certain types of observations, requiring teams to check early on that they can turn to and from their observation attitude, and that their attitude is safe, and adjusting the Implementation process to give more time for science observation designers. This paper will discuss these process changes and lessons learned so far.
{"title":"The Cassini grand finale mission: Planning for a new mission environment","authors":"N. Vandermey, William M. Heventhal, T. Ray","doi":"10.1109/AERO.2017.7943877","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943877","url":null,"abstract":"The Cassini F-Ring & Proximal Orbits (FRPO) is a new and unique mission; to ensure the highest priority science gets implemented, the POST (Proximal Orbit Science Team) was created to pre-allocate the time around periapse for all 22 proximal orbits. The F-ring orbits, and proximal time outside of POST, were handled similar to Cassini's Solstice Mission using the Pre-Integrated Event (PIE) process. The new and unique properties of the spacecraft's trajectory required much forethought to be flown safely while still planning for the most and best science return possible. Some ring-plane crossings (RPX) will be protected against dust impacts by turning the high gain antenna (HGA) to the dust RAM direction (HGA2RAM). If on the first proximal RPX higher than expected dust readings are seen then the Project Office may choose to require more (all) subsequent RPX to be HGA2RAM, implemented via a real-time command overlay for uplinked sequences. The pointing uncertainties will be larger than usual after the final targeted flyby; some of the process changes to address this include adding extra orbit trim maneuvers (OTMs) (fuel permitting) to resync to the reference trajectory and reduce pointing uncertainties; and movable blocks of commands to be used for some periapses where atmospheric drag may cause large timing shifts Changes made for FRPO to address perceptions that these sequences will be hard to implement include requiring early pointing designs (during integration) for certain types of observations, requiring teams to check early on that they can turn to and from their observation attitude, and that their attitude is safe, and adjusting the Implementation process to give more time for science observation designers. This paper will discuss these process changes and lessons learned so far.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133257246","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943623
Ilana Gat, T. Talon
Deep-space travel is limited by the costly voyage to leave Earth's atmosphere and gravitational pull. The volume of propellants per unit mass of the payload required for that segment constrains the payload size and payload destination. To circumvent that limitation, this paper presents the feasibility of a refueling station using Lunar resources, called Lunarport. On Earth's moon, an unmanned station will robotically mine, produce, and store fuel and oxidizer from water ice at the poles. A first-stage-like rocket, called the Lunar Resupply Shuttle (LRS), stationed there and propelled with mined resources, will launch and dock with a passing payload-carrying rocket. That rocket will be reloaded with propellants by the LRS, after which the LRS will detach and the payload-carrying rocket will continue its journey to its desired trajectory. The LRS will wait in Lower Lunar Orbit (LLO, to avoid deterioration from Lunar regolith) until another payload-carrying rocket is launched from Earth, after which, the LRS will land back on the Moon, reload propellants, and launch again to dock with the next rocket. This paper elaborates on Lunarport, presenting proof-of-concept calculations of the increase in payload size sent to various payload destinations as well as a cost-benefit analysis. By way of example, NASA's Space Launch System (SLS) en-route to Mars that refuels at Lunarport can have a payload approximately 17 metric tons (mT) heavier than one traveling straight to Mars from Earth. This increase of more than 50% [1] is just to a relatively nearby planet — Mars. Sending a payload farther offers larger benefits with Lunarport. Wear-and-tear issues the port will be subjected to are also discussed. A full analysis of Lunarport will be done during the 2017 Caltech Space Challenge sponsored by Airbus Defence and Space held from March 26–31, 2017.
{"title":"Lunarport: A proposed Lunar-resource station to expand deep-space travel horizons","authors":"Ilana Gat, T. Talon","doi":"10.1109/AERO.2017.7943623","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943623","url":null,"abstract":"Deep-space travel is limited by the costly voyage to leave Earth's atmosphere and gravitational pull. The volume of propellants per unit mass of the payload required for that segment constrains the payload size and payload destination. To circumvent that limitation, this paper presents the feasibility of a refueling station using Lunar resources, called Lunarport. On Earth's moon, an unmanned station will robotically mine, produce, and store fuel and oxidizer from water ice at the poles. A first-stage-like rocket, called the Lunar Resupply Shuttle (LRS), stationed there and propelled with mined resources, will launch and dock with a passing payload-carrying rocket. That rocket will be reloaded with propellants by the LRS, after which the LRS will detach and the payload-carrying rocket will continue its journey to its desired trajectory. The LRS will wait in Lower Lunar Orbit (LLO, to avoid deterioration from Lunar regolith) until another payload-carrying rocket is launched from Earth, after which, the LRS will land back on the Moon, reload propellants, and launch again to dock with the next rocket. This paper elaborates on Lunarport, presenting proof-of-concept calculations of the increase in payload size sent to various payload destinations as well as a cost-benefit analysis. By way of example, NASA's Space Launch System (SLS) en-route to Mars that refuels at Lunarport can have a payload approximately 17 metric tons (mT) heavier than one traveling straight to Mars from Earth. This increase of more than 50% [1] is just to a relatively nearby planet — Mars. Sending a payload farther offers larger benefits with Lunarport. Wear-and-tear issues the port will be subjected to are also discussed. A full analysis of Lunarport will be done during the 2017 Caltech Space Challenge sponsored by Airbus Defence and Space held from March 26–31, 2017.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"116 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130067534","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943574
Dayton L. Jones, J. Romney, V. Dhawan, W. Folkner, R. Jacobson, C. Jacobs, E. Fomalont
The Cassini spacecraft has been in orbit about Saturn since 2004. During this time, regular astrometric measurements of Cassini's sky position have been made with the Very Long Baseline Array (VLBA). These are high precision differential measurements that determine the position of Cassini with respect to angularly nearby extragalactic radio sources. Differential, narrow-angle astrometry reduces many error sources, particularly those associated with signal propagation effects in the ionosphere and troposphere. The background radio sources positions are tied to the inertial International Celestial Reference Frame (ICRF) by other international VLBI observations. Thus, we obtain a series of ICRF positions for Cassini, which can be combined with spacecraft orbit solutions from Deep Space Network Doppler tracking to get ICRF positions for the center of mass of the Saturn system. These positions have typical accuracies at the nano-radian level. For some epochs uncertainties in the background source positions are a major component of the total error, but these positions are being constantly improved as additional VLBI observations are incorporated into radio source catalogs. The planetary ephemeris group at the Jet Propulsion Laboratory uses our position measurements to fit improved orbital solutions for Saturn. As a result the orientation of the plane of Saturn's orbit is now known to approximately 0.25 milli-arcseconds (1.25 nrad), nearly an order of magnitude improvement over its pre-VLBA uncertainty. We will continue this observing program until the end of the Cassini mission in late 2017. By that time we will have covered about 1/3 of Saturn's orbital longitude range. Future improvements to this technique will include the use of higher spacecraft downlink frequencies (Ka band instead of X band) and higher ground array sensitivity to permit the use of weaker but angularly closer reference sources. In addition, the continuing international campaigns to enhance the accuracy of radio source catalogs will be extended to weaker sources, improving their ties to the ICRF.
{"title":"A decade of astrometric observations of Cassini: Past results and future prospects","authors":"Dayton L. Jones, J. Romney, V. Dhawan, W. Folkner, R. Jacobson, C. Jacobs, E. Fomalont","doi":"10.1109/AERO.2017.7943574","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943574","url":null,"abstract":"The Cassini spacecraft has been in orbit about Saturn since 2004. During this time, regular astrometric measurements of Cassini's sky position have been made with the Very Long Baseline Array (VLBA). These are high precision differential measurements that determine the position of Cassini with respect to angularly nearby extragalactic radio sources. Differential, narrow-angle astrometry reduces many error sources, particularly those associated with signal propagation effects in the ionosphere and troposphere. The background radio sources positions are tied to the inertial International Celestial Reference Frame (ICRF) by other international VLBI observations. Thus, we obtain a series of ICRF positions for Cassini, which can be combined with spacecraft orbit solutions from Deep Space Network Doppler tracking to get ICRF positions for the center of mass of the Saturn system. These positions have typical accuracies at the nano-radian level. For some epochs uncertainties in the background source positions are a major component of the total error, but these positions are being constantly improved as additional VLBI observations are incorporated into radio source catalogs. The planetary ephemeris group at the Jet Propulsion Laboratory uses our position measurements to fit improved orbital solutions for Saturn. As a result the orientation of the plane of Saturn's orbit is now known to approximately 0.25 milli-arcseconds (1.25 nrad), nearly an order of magnitude improvement over its pre-VLBA uncertainty. We will continue this observing program until the end of the Cassini mission in late 2017. By that time we will have covered about 1/3 of Saturn's orbital longitude range. Future improvements to this technique will include the use of higher spacecraft downlink frequencies (Ka band instead of X band) and higher ground array sensitivity to permit the use of weaker but angularly closer reference sources. In addition, the continuing international campaigns to enhance the accuracy of radio source catalogs will be extended to weaker sources, improving their ties to the ICRF.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129839125","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943819
D. Israel, B. Edwards, J. Staren
This paper provides a concept for an evolution of NASA's optical communications near Earth relay architecture. NASA's Laser Communications Relay Demonstration (LCRD), a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory — California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). LCRD will provide a minimum of two years of high data rate optical communications service experiments in geosynchronous orbit (GEO), following launch in 2019. This paper will provide an update of the LCRD mission status and planned capabilities and experiments, followed by a discussion of the path from LCRD to operational network capabilities.
{"title":"Laser Communications Relay Demonstration (LCRD) update and the path towards optical relay operations","authors":"D. Israel, B. Edwards, J. Staren","doi":"10.1109/AERO.2017.7943819","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943819","url":null,"abstract":"This paper provides a concept for an evolution of NASA's optical communications near Earth relay architecture. NASA's Laser Communications Relay Demonstration (LCRD), a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory — California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). LCRD will provide a minimum of two years of high data rate optical communications service experiments in geosynchronous orbit (GEO), following launch in 2019. This paper will provide an update of the LCRD mission status and planned capabilities and experiments, followed by a discussion of the path from LCRD to operational network capabilities.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126135428","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943732
A. Erlank, Christopher P. Bridges
While small, low-cost satellites continue to increase in capability and popularity, their reliability remains a problem. Traditional techniques for increasing system reliability are well known to satellite developers, however, their implementation on low-cost satellites is often limited due to intrinsic mass, volume and budgetary restrictions. Aiming for graceful degeneration, therefore, may be a more promising route. To this end, a stem-cell-inspired, multicellular architecture is being developed using commercial-off-the-shelf components. It aims to replace a significant portion of a typical satellite's bus avionics with a set of initially identical cells. Analogous to biological cells, the artificial cells are able to differentiate during runtime to take on a variety of tasks thanks to a set of artificial proteins. Each cell reconfigures its own proteins within the context of a system-wide distributed task management strategy. In this way, essential tasks can be maintained, even as system cells fail. This paper focusses on two hardware implementations of the stem-cell inspired architecture. The first implementation, based on a single cell, serves as the Payload Interface Computer on a CubeSat named SME-SAT. The second hardware implementation is a benchtop system composed of several cells intended to demonstrate a complete multicellular system in operation. In order to demonstrate the feasibility of these multicellular architectures, the physical attributes of the hardware implementations are compared to those of more traditional implementations and are shown to have enhanced reliability at the cost of increased power and internal bus bandwidth.
{"title":"Satellite stem cells: The benefits & overheads of reliable, multicellular architectures","authors":"A. Erlank, Christopher P. Bridges","doi":"10.1109/AERO.2017.7943732","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943732","url":null,"abstract":"While small, low-cost satellites continue to increase in capability and popularity, their reliability remains a problem. Traditional techniques for increasing system reliability are well known to satellite developers, however, their implementation on low-cost satellites is often limited due to intrinsic mass, volume and budgetary restrictions. Aiming for graceful degeneration, therefore, may be a more promising route. To this end, a stem-cell-inspired, multicellular architecture is being developed using commercial-off-the-shelf components. It aims to replace a significant portion of a typical satellite's bus avionics with a set of initially identical cells. Analogous to biological cells, the artificial cells are able to differentiate during runtime to take on a variety of tasks thanks to a set of artificial proteins. Each cell reconfigures its own proteins within the context of a system-wide distributed task management strategy. In this way, essential tasks can be maintained, even as system cells fail. This paper focusses on two hardware implementations of the stem-cell inspired architecture. The first implementation, based on a single cell, serves as the Payload Interface Computer on a CubeSat named SME-SAT. The second hardware implementation is a benchtop system composed of several cells intended to demonstrate a complete multicellular system in operation. In order to demonstrate the feasibility of these multicellular architectures, the physical attributes of the hardware implementations are compared to those of more traditional implementations and are shown to have enhanced reliability at the cost of increased power and internal bus bandwidth.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153941","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943571
C. Wargo, B. Capozzi, Michael Graham, Dylan Hasson, J. Glaneuski, Brandon Van Acker
Numerous parties have a desire to operate Unmanned Aircraft Systems (UASs)1 and small UASs (known as “sUAS”) in the complex terminal environment and on the airport surface. New and increasingly available surveillance technologies, data link driven controller instructions such as D-TAXI, and access to NAS system information via SWIM (System Wide Information Management) are potential means to enhance the ability of the UAS Pilot in Command's (PICs) to integrate and operate safely in the terminal environment. Vendors directly connected to SWIM feeds can receive ASDE-X data from equipped airports. Vendors also connect to other NAS data feeds for flight planning, airport status, weather information, and traffic flow management initiatives. These data feeds are transitioning to new formats consistent with international standards. All of these information streams are able to provide the Remote PIC with better Situational Awareness (SA) and the ability to better understand the relationship of their aircraft to other aircraft movements; all of which will assist in maintaining the efficiency of NAS operations as well as the speed and tempo of airports operations. Future airport area surveillance information sources from ADS-B and from Ground Based Sense and Avoid (GBSAA) solutions are also emerging. Enhanced vision technologies for Remotely Piloted Aircraft (RPA) are being deployed to support reduced visibility operations. Additionally, autonomous technologies are being researched to control aircraft movement on the airport surface. Specific pilot alerts are being developed for surface events, such as conformance to taxi path, failure of other aircraft to hold for crossing clearances, or intersection encroachments. This paper provides an integrated view of how these emerging technologies can be leveraged to support the Remote PIC and the UAS operations in congested terminal airspace and on airport surface operations.
{"title":"Enhancing UAS Pilot safety by terminal and airport shared information situational awareness","authors":"C. Wargo, B. Capozzi, Michael Graham, Dylan Hasson, J. Glaneuski, Brandon Van Acker","doi":"10.1109/AERO.2017.7943571","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943571","url":null,"abstract":"Numerous parties have a desire to operate Unmanned Aircraft Systems (UASs)1 and small UASs (known as “sUAS”) in the complex terminal environment and on the airport surface. New and increasingly available surveillance technologies, data link driven controller instructions such as D-TAXI, and access to NAS system information via SWIM (System Wide Information Management) are potential means to enhance the ability of the UAS Pilot in Command's (PICs) to integrate and operate safely in the terminal environment. Vendors directly connected to SWIM feeds can receive ASDE-X data from equipped airports. Vendors also connect to other NAS data feeds for flight planning, airport status, weather information, and traffic flow management initiatives. These data feeds are transitioning to new formats consistent with international standards. All of these information streams are able to provide the Remote PIC with better Situational Awareness (SA) and the ability to better understand the relationship of their aircraft to other aircraft movements; all of which will assist in maintaining the efficiency of NAS operations as well as the speed and tempo of airports operations. Future airport area surveillance information sources from ADS-B and from Ground Based Sense and Avoid (GBSAA) solutions are also emerging. Enhanced vision technologies for Remotely Piloted Aircraft (RPA) are being deployed to support reduced visibility operations. Additionally, autonomous technologies are being researched to control aircraft movement on the airport surface. Specific pilot alerts are being developed for surface events, such as conformance to taxi path, failure of other aircraft to hold for crossing clearances, or intersection encroachments. This paper provides an integrated view of how these emerging technologies can be leveraged to support the Remote PIC and the UAS operations in congested terminal airspace and on airport surface operations.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130456213","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943684
D. Lorenz, R. Olds, A. May, C. Mario, M. Perry, E. Palmer, M. Daly
The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) spacecraft launched on September 8, 2016 to embark on an asteroid sample return mission. It is expected to rendezvous with the asteroid, Bennu, navigate to the surface, collect a sample (July'20), and return the sample to Earth (September'23). The original mission design called for using one of two Flash Lidar units to provide autonomous navigation to the surface. Following Preliminary design and initial development of the Lidars, reliability issues with the hardware and test program prompted the project to begin development of an alternative navigation technique to be used as a backup to the Lidar. At the critical design review, Natural Feature Tracking (NFT) was added to the mission. NFT is an onboard optical navigation system that compares observed images to a set of asteroid terrain models which are rendered in real-time from a catalog stored in memory on the flight computer. Onboard knowledge of the spacecraft state is then updated by a Kalman filter using the measured residuals between the rendered reference images and the actual observed images. The asteroid terrain models used by NFT are built from a shape model generated from observations collected during earlier phases of the mission and include both terrain shape and albedo information about the asteroid surface. As a result, the success of NFT is dependent on selecting a set of topographic features that can be both identified during descent as well as reliably rendered using the shape model data available. During development, the OSIRIS-REx team faced significant challenges in developing a process conducive to robust operation. This was especially true for terrain models to be used as the spacecraft gets close to the asteroid and higher fidelity models are required for reliable image correlation. This paper will present some of the challenges and lessons learned from the development of the NFT system which includes not just the flight hardware and software but the development of the terrain models used to generate the onboard rendered images.
{"title":"Lessons learned from OSIRIS-REx autonomous navigation using natural feature tracking","authors":"D. Lorenz, R. Olds, A. May, C. Mario, M. Perry, E. Palmer, M. Daly","doi":"10.1109/AERO.2017.7943684","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943684","url":null,"abstract":"The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) spacecraft launched on September 8, 2016 to embark on an asteroid sample return mission. It is expected to rendezvous with the asteroid, Bennu, navigate to the surface, collect a sample (July'20), and return the sample to Earth (September'23). The original mission design called for using one of two Flash Lidar units to provide autonomous navigation to the surface. Following Preliminary design and initial development of the Lidars, reliability issues with the hardware and test program prompted the project to begin development of an alternative navigation technique to be used as a backup to the Lidar. At the critical design review, Natural Feature Tracking (NFT) was added to the mission. NFT is an onboard optical navigation system that compares observed images to a set of asteroid terrain models which are rendered in real-time from a catalog stored in memory on the flight computer. Onboard knowledge of the spacecraft state is then updated by a Kalman filter using the measured residuals between the rendered reference images and the actual observed images. The asteroid terrain models used by NFT are built from a shape model generated from observations collected during earlier phases of the mission and include both terrain shape and albedo information about the asteroid surface. As a result, the success of NFT is dependent on selecting a set of topographic features that can be both identified during descent as well as reliably rendered using the shape model data available. During development, the OSIRIS-REx team faced significant challenges in developing a process conducive to robust operation. This was especially true for terrain models to be used as the spacecraft gets close to the asteroid and higher fidelity models are required for reliable image correlation. This paper will present some of the challenges and lessons learned from the development of the NFT system which includes not just the flight hardware and software but the development of the terrain models used to generate the onboard rendered images.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130666588","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943593
B. Alsalam, K. Morton, D. Campbell, Felipe Gonzalez
In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.
{"title":"Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture","authors":"B. Alsalam, K. Morton, D. Campbell, Felipe Gonzalez","doi":"10.1109/AERO.2017.7943593","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943593","url":null,"abstract":"In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116921129","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}
Pub Date : 2017-03-04DOI: 10.1109/AERO.2017.7943974
T. Setterfield, David W. Miller, J. Leonard, A. Saenz-Otero
This paper presents a method of obtaining the maximum a posteriori estimate of an inspector satellite's trajectory about an unknown tumbling target while on-orbit. An inspector equipped with radar or a 3D visual sensor (such as LiDAR or stereo cameras), an inertial measurement unit, and a star tracker is used to obtain measurements of range and bearing to the target's centroid, angular velocity, acceleration, and orientation in the inertial frame. A smoothing-based trajectory estimation scheme is presented that makes use of all the input sensor data to estimate the inspector's trajectory. Open-source incremental smoothing and mapping (iSAM2) software is used to implement the smoothing-based trajectory estimation algorithm; this facilitates computationally efficient evaluation of the entire trajectory, which can be performed incrementally, and in real time on a computer capable of processing 3D visual sensor data in real time. The presented algorithm was tested on data obtained in 6 degree-of-freedom microgravity using the SPHERES-VERTIGO robotic test platform on the International Space Station (ISS). In these tests, a SPHERES inspector satellite with attached stereo cameras circumnavigated a passive SPHERES target satellite, making visual observations of it. The results of these tests demonstrate accurate estimation of the inspector satellite's trajectory.
{"title":"Smoothing-based estimation of an inspector satellite trajectory relative to a passive object","authors":"T. Setterfield, David W. Miller, J. Leonard, A. Saenz-Otero","doi":"10.1109/AERO.2017.7943974","DOIUrl":"https://doi.org/10.1109/AERO.2017.7943974","url":null,"abstract":"This paper presents a method of obtaining the maximum a posteriori estimate of an inspector satellite's trajectory about an unknown tumbling target while on-orbit. An inspector equipped with radar or a 3D visual sensor (such as LiDAR or stereo cameras), an inertial measurement unit, and a star tracker is used to obtain measurements of range and bearing to the target's centroid, angular velocity, acceleration, and orientation in the inertial frame. A smoothing-based trajectory estimation scheme is presented that makes use of all the input sensor data to estimate the inspector's trajectory. Open-source incremental smoothing and mapping (iSAM2) software is used to implement the smoothing-based trajectory estimation algorithm; this facilitates computationally efficient evaluation of the entire trajectory, which can be performed incrementally, and in real time on a computer capable of processing 3D visual sensor data in real time. The presented algorithm was tested on data obtained in 6 degree-of-freedom microgravity using the SPHERES-VERTIGO robotic test platform on the International Space Station (ISS). In these tests, a SPHERES inspector satellite with attached stereo cameras circumnavigated a passive SPHERES target satellite, making visual observations of it. The results of these tests demonstrate accurate estimation of the inspector satellite's trajectory.","PeriodicalId":224475,"journal":{"name":"2017 IEEE Aerospace Conference","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117091386","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}
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