CUTTING THROUGH THE NOISE
that are more encompassing onto that to give the EWOs a wider range of what they can identify, said Monto.
While the EWOs would remain as the lead for identifying signals of interest and analyzing their impact, the use of artificial intel- ligence and machine learning could help them quickly and accurately detect patterns, identify signals of significance, filter out unwanted signal noise and paint a picture of the electromag- netic spectrum.
THE CHALLENGE RCO’s Army Signal Classification Challenge began April 30 and closed Aug. 13. After opening registration online, competitors were given access to the training data set, consisting of more than 4.3 million instances across 24 different modulations, which included a noise class. (Te noise class represents “white” noise to replicate the real-life environment that signals would be detected in, rather than a pristine lab environment.) Te effort sought solutions that could perform “blind” signal classification quickly and accurately. Blind signal classification requires little to no prior knowledge about the signal being detected in that specific instance. Instead, the solution would automatically classify the modulation, or change of a radio frequency waveform, as a first step toward signal classification.
Te challenge gave participants 90 days to develop their models and to work with the training data sets. Tat was followed by two test data sets of varying complexity that were the basis for judging submissions. Te first data set was released 67 days after the challenge launch, with a solution submission window of 15 days. A second, more complex test data set was released 84 days after the challenge launch, with a shorter submission window of only seven days.
Participants’ scores were based on a combined weighted score for both test data sets. Competitors could see how well they were performing against their peers through a participant leader board that showed scores in real time.
For first-place winners Team Platypus—which participated in the Defense Advanced Research Projects Agency’s Software Defined Radio Hackfest 2017 and whose name references platypuses’ abil- ity to detect electrical fields with their bills—the challenge lined up perfectly with its core research in artificial intelligence and advanced signal processing.
“We really enjoyed the challenge process, which included the hard problem curation, providing training data and a specific scoring
74 Army AL&T Magazine January-March 2019
algorithm,” Vila said. “To do this with the highest level of confi- dence, we had to use a multipronged approach. We built statistics and metrics inspired by communication principles, and we also developed deep learning classifiers that work directly on the raw data. We ended up using several state-of-the-art AI techniques to achieve the winning submission.”
Teir technology includes an algorithm trained to identify what kind of signal is present in the midst of a congested radio frequency environment, much like Soldiers would find in an urban core or battlefield where both friendly and enemy radio communications are being detected.
CONCLUSION By structuring this effort as a challenge and not going through the traditional RFI process, RCO proved it could take an indus- try model and move fast. For its efforts, it is substantially closer to identifying a potential solution that could be applied to battlefield electronic warfare capabilities in the very near future. Te challenge also showed that RCO could harness the prom- ise of artificial intelligence and machine learning by applying it to a specific problem. Te amount of interest and quality of performance, including from nontraditional organizations, was remarkable.
Now, RCO is quickly moving forward to the next step, with two possible options. First, RCO could initiate a second, more intense challenge and open it up to only the top performers in the first challenge. Or, RCO could begin to immediately move the algo- rithms into the hands of Soldiers through software enhancements to their existing electronic warfare equipment. Tis would enable the Soldiers to give immediate feedback and enable the Army to incrementally build capability.
Over the next several months, RCO will begin to advance what was learned from the challenge, potentially prototyping the lead- ing artificial intelligence and machine learning algorithms into Army electronic warfare systems.
For more information on the Army RCO,
rapidcapabilitiesoffice.army.mil/.
NANCY JONES-BONBREST is a public communications specialist for the Army RCO and has written extensively about Army modern- ization and acquisition for several years, including multiple training and testing events. She holds a B.S. in journalism from the University of Maryland, College Park.
+ go to http://
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152
