{"id":1838,"date":"2020-08-09T17:52:30","date_gmt":"2020-08-09T17:52:30","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/08\/09\/lockheed-martin-usc-build-smart-cubesats-la-jument\/"},"modified":"2020-08-09T17:52:30","modified_gmt":"2020-08-09T17:52:30","slug":"lockheed-martin-usc-build-smart-cubesats-la-jument","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/08\/09\/lockheed-martin-usc-build-smart-cubesats-la-jument\/","title":{"rendered":"Lockheed Martin, USC build smart CubeSats, La Jument"},"content":{"rendered":"

Lockheed Martin, USC build smart CubeSats, La Jument<\/a><\/p>\n

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<\/a>
An artists\u2019 rendering of the La Jument nanosatellite. (University of Southern California illustration)
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Lockheed Martin is building mission payloads for a Space Engineering Research Center at University of Southern California Information Sciences Institute small satellite program called La Jument, which enhance Artificial Intelligence and Machine Learning space technologies.<\/p>\n

For the program, four La Jument nanosatellites \u2014 the first launching later this year \u2014 will use Lockheed Martin\u2019s SmartSat\u2122 software-defined satellite architecture on both their payload and bus. SmartSat lets satellite operators quickly change missions while in orbit with the simplicity of starting, stopping or uploading new applications.<\/p>\n

The system is powered by the NVIDIA\u00ae Jetson\u2122 platform built on the CUDA-X\u2122 capable software stack and supported by the NVIDIA JetPack\u2122 software development kit (SDK), delivering powerful AI at the edge computing capabilities to unlock advanced image and digital signal processing.<\/p>\n

SmartSat\u2122 provides on-board cyber threat detection, while the software-defined payload houses advanced optical and infrared cameras utilized by Lockheed Martin\u2019s Advanced Technology Center (ATC) to further mature and space qualify Artificial Intelligence (AI) and Machine Learning (ML) technologies. The La Jument payloads are the latest of more than 300 payloads Lockheed Martin has built for customers.<\/p>\n

\u201cLa Jument and SmartSat are pushing new boundaries of what is possible in space when you adopt an open software architecture that lets you change missions on the fly,\u201d said Adam Johnson, Director of SmartSat\u2122 and La Jument at Lockheed Martin Space. \u201cWe are excited to release a SmartSat software development kit (SDK) to encourage developers to write their own third-party mission apps and offer an orbital test-bed.\u201d<\/p>\n

La Jument satellites will enable AI\/ML algorithms in orbit because of advanced multi-core processing and on-board graphics processing units (GPU). One app being tested in orbit will be SuperRes, an algorithm developed by Lockheed Martin that can automatically enhance the quality of an image, like some smartphone camera apps. SuperRes enables exploitation and detection of imagery produced by lower-cost, lower-quality image sensors.<\/p>\n

\u201cWe were able to design, build and integrate the first payload for La Jument in five months,\u201d said Sonia Phares, Vice President of Engineering and Technology at Lockheed Martin Space. \u201cSatellites like this demonstrate our approach to rapid development and innovation that lets us solve our customers\u2019 toughest challenges faster than ever.\u201d<\/p>\n

The first of the four La Jument nanosatellites is a student-designed and built 1.5U CubeSat that will be launched with a SmartSat payload to test the complete system from ground to space, including ground station communications links and commanding SmartSat infrastructure while in-orbit. The second is a 3U nanosat, the size of three small milk cartons stacked on top of each other, with optical payloads connected to SmartSat that will allow AI\/ML in-orbit testing. Finally, two 6U CubeSats are being designed jointly with USC that will be launched mid-2022. The pair will launch together and incorporate future research from USC and Lockheed Martin, including new SmartSat apps, sensors and bus technologies.
 
 
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Published at Sun, 09 Aug 2020 16:30:00 +0000<\/p>\n

Lockheed Martin, USC build smart CubeSats, La Jument<\/a><\/p>\n

\n
<\/a>
An artists\u2019 rendering of the La Jument nanosatellite. (University of Southern California illustration)
\n<\/figcaption><\/figure>\n<\/div>\n
Advertisement<\/span>\n<\/div>\n

Lockheed Martin is building mission payloads for a Space Engineering Research Center at University of Southern California Information Sciences Institute small satellite program called La Jument, which enhance Artificial Intelligence and Machine Learning space technologies.<\/p>\n

For the program, four La Jument nanosatellites \u2014 the first launching later this year \u2014 will use Lockheed Martin\u2019s SmartSat\u2122 software-defined satellite architecture on both their payload and bus. SmartSat lets satellite operators quickly change missions while in orbit with the simplicity of starting, stopping or uploading new applications.<\/p>\n

The system is powered by the NVIDIA\u00ae Jetson\u2122 platform built on the CUDA-X\u2122 capable software stack and supported by the NVIDIA JetPack\u2122 software development kit (SDK), delivering powerful AI at the edge computing capabilities to unlock advanced image and digital signal processing.<\/p>\n

SmartSat\u2122 provides on-board cyber threat detection, while the software-defined payload houses advanced optical and infrared cameras utilized by Lockheed Martin\u2019s Advanced Technology Center (ATC) to further mature and space qualify Artificial Intelligence (AI) and Machine Learning (ML) technologies. The La Jument payloads are the latest of more than 300 payloads Lockheed Martin has built for customers.<\/p>\n

\u201cLa Jument and SmartSat are pushing new boundaries of what is possible in space when you adopt an open software architecture that lets you change missions on the fly,\u201d said Adam Johnson, Director of SmartSat\u2122 and La Jument at Lockheed Martin Space. \u201cWe are excited to release a SmartSat software development kit (SDK) to encourage developers to write their own third-party mission apps and offer an orbital test-bed.\u201d<\/p>\n

La Jument satellites will enable AI\/ML algorithms in orbit because of advanced multi-core processing and on-board graphics processing units (GPU). One app being tested in orbit will be SuperRes, an algorithm developed by Lockheed Martin that can automatically enhance the quality of an image, like some smartphone camera apps. SuperRes enables exploitation and detection of imagery produced by lower-cost, lower-quality image sensors.<\/p>\n

\u201cWe were able to design, build and integrate the first payload for La Jument in five months,\u201d said Sonia Phares, Vice President of Engineering and Technology at Lockheed Martin Space. \u201cSatellites like this demonstrate our approach to rapid development and innovation that lets us solve our customers\u2019 toughest challenges faster than ever.\u201d<\/p>\n

The first of the four La Jument nanosatellites is a student-designed and built 1.5U CubeSat that will be launched with a SmartSat payload to test the complete system from ground to space, including ground station communications links and commanding SmartSat infrastructure while in-orbit. The second is a 3U nanosat, the size of three small milk cartons stacked on top of each other, with optical payloads connected to SmartSat that will allow AI\/ML in-orbit testing. Finally, two 6U CubeSats are being designed jointly with USC that will be launched mid-2022. The pair will launch together and incorporate future research from USC and Lockheed Martin, including new SmartSat apps, sensors and bus technologies.
 
 
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Published at Sun, 09 Aug 2020 16:30:00 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

Lockheed Martin, USC build smart CubeSats, La Jument An artists\u2019 rendering of the La Jument…<\/p>\n","protected":false},"author":3,"featured_media":1837,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1838","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/08\/OHSDOD.jpg?fit=602%2C431&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-tE","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/1838","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=1838"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/1838\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/1837"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=1838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=1838"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=1838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}