WASHINGTON \u2014 By the end of 2018, the Air Force\u2019s distributed common ground system will have tested a new open architecture backbone, paving the way for it to use algorithms and machine learning to exploit data, a service official told Defense News on Sept. 5.<\/p>\n\n\n\n
The Distributed Common Ground System is the service\u2019s primary intelligence processing system, collecting imagery, video feeds and signals from platforms like the U-2 spy plane, the RQ-4 Global Hawk and the MQ-9 Reaper. But the system, stood up in the mid 1990s, has a closed architecture, making it difficult to modify.<\/p>\n\n\n\n
Once the DCGS open architecture backbone is complete, the Air Force will be able to upload new technologies to the system \u2014 such as algorithms that can interpret data and reduce the workload of intelligence analysts \u2014 much like a smartphone downloads apps, said Kenneth Bray, acting associate deputy chief of staff for intelligence, surveillance and reconnaissance.<\/p>\n\n\n\n
The Air Force is taking a piecemeal approach to implementing the open architecture \u2014 for instance, testing the approach on DCGS video feeds at Beale Air Force Base, California, and then testing it on the signals intelligence feed at another base, he said.<\/p>\n\n\n\n
\u201cThey just finished the first big operational demonstration, and we\u2019re fairly satisfied with how it turned out. With anything, there are a few things to learn from it, so we\u2019re going to have to go back and make some corrections, but we\u2019re heading on to the next step,\u201d Bray said.<\/p>\n\n\n\n
\u201cBy this time next year, we will have tested all the pieces,\u201d and the Air Force can begin rolling out the changes to all 27 DCGS sites.<\/p>\n\n\n\n
For decades, the Air Force has manually had to analyze data coming into DSGS and other intelligence processing systems. That labor-intensive process often meant analysts were simply answering the question, \u201cwhat is happening?\u201d instead of finding patterns or determining what it will be doing next.<\/p>\n\n\n\n
\u201cYou\u2019d be sitting [and] looking at your imagery, I\u2019d be sitting [and] listening to my signals intelligence, and you\u2019d be talking \u2014 literally \u2014 to me,\u201d he said. \u201cThere are algorithms you can write that can manually put that together. So instead of watching an entire chain of events unfold for 30 minutes and your finally arrive at an a-ha moment between you and myself, the a-ha moment can happen in five minutes or half a minute or five seconds.\u201d<\/p>\n\n\n\n
The Air Force\u2019s intelligence community isn\u2019t the only part of the service struggling to digest the massive amounts of data coming in from aircraft, drones, satellites and other platforms. Air Force Chief of Staff David Goldfein has often criticized the segmented, stovepiped nature of its command and control enterprise, saying that the service needs to incorporate autonomy and artificial intelligence to help operators make decisions faster.<\/p>\n\n\n\n
As the Air Force works to transition DCGS to an open architecture, it is also helping the Defense Department lay the foundation to make DCGS more automated. Through an effort called Project Maven, the department hopes to collect a pool of \u201cgood data\u201d that algorithms can process and adapt to \u2014 a process called machine learning.<\/p>\n\n\n\n
\u201cIt we can get our enterprise turned over to labeling all the data that it currently generates, then all that data then becomes searchable and manipulated by any number of algorithms,\u201d Bray explained.<\/p>\n\n\n\n
The hope is that in the future, intelligence systems like DCGS will not only be able to help analysts find critical information\u2014say, a truck linked to a terrorist group\u2014it will be able to pull up every other time that truck has been seen by the Air Force over the past year.<\/p>\n\n\n\n
\u201cThat\u2019s something that in the past would have taken six months or a year to go manually track where has that vehicle been in the last year.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
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