Collaborating Authors

AI research strengthens certainty in battlefield decision-making


A new framework for neural networks' processing enables artificial intelligence to better judge objects and potential threats in hostile environments. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and university partners from the Internet of Battlefield Things Collaborative Research Alliance, or IoBT CRA, developed a method for neural networks to be more confident in their understanding of battlefield environments. To achieve this, researchers reviewed frameworks to represent uncertainty, categorized sources of uncertainty in military information-networks' common operating environment, and most importantly created solutions to manage uncertainty within systems. The researchers developed insights from the uncertainty management approaches into a workflow that maximizes effectiveness in accomplishing mission goals despite the presence of uncertainty in data inputs. Through this process, they teach neural networks when to say, "I am sure," and be right about it.

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar Machine Learning

Abstract--In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the -shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a con gested spectral environment, and the ability to share 100MHz spect rum with an uncooperative communications system. We examine po licy iteration, which solves an environment posed as a Markov Dec ision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well a s Deep RL techniques, which utilize a form of Q -Learning to approximate a parameterized function that is used by the rad ar to select optimal actions. We show that RL techniques are benefi cial over a Sense-and-A void (SAA) scheme and discuss the conditi ons under which each approach is most effective. The Third Generation Partnership Project (3GPP) has recently received FCC approval to support 5G New Radio (NR) operation in sub-6 GHz frequency bands that are heavily utilized by radar systems [1], [2]. Thus, there is a significa nt need for radar systems capable of dynamic spectrum sharing.

Army researchers create pioneering approach to real-time conversational AI


Spoken dialogue is the most natural way for people to interact with complex autonomous agents such as robots. Future Army operational environments will require technology that allows artificial intelligent agents to understand and carry out commands and interact with them as teammates. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and the University of Southern California's Institute for Creative Technologies, a Department of Defense-sponsored University Affiliated Research Center, created an approach to flexibly interpret and respond to Soldier intent derived from spoken dialogue with autonomous systems. This technology is currently the primary component for dialogue processing for the lab's Joint Understanding and Dialogue Interface, or JUDI, system, a prototype that enables bi-directional conversational interactions between Soldiers and autonomous systems. "We employed a statistical classification technique for enabling conversational AI using state-of-the-art natural language understanding and dialogue management technologies," said Army researcher Dr. Felix Gervits.

AI may be better for detecting radar signals, facilitating spectrum sharing


In a new paper, NIST researchers demonstrate that deep learning algorithms -- a form of artificial intelligence -- are significantly better than a commonly used, less sophisticated method for detecting when offshore radars are operating. Improved radar detection would enable commercial users to know when they must yield the so-called 3.5 Gigahertz (3.5 GHz) Band. In 2015, the FCC adopted rules for the Citizens Broadband Radio Service (CBRS) to permit commercial LTE (long-term evolution) wireless equipment vendors and service providers to use the 3.5 GHz Band when not needed for radar operations. Companies such as AT&T, Google, Nokia, Qualcomm, Sony and Verizon have been eager to access this band (between 3550 and 3700 MHz) because it will expand product markets and give end users better coverage and higher data rate speeds in a variety of environments where service is traditionally weak. NIST helped develop 10 standard specifications that enable service providers and other potential users to operate in the 3.5 GHz Band under FCC regulations while assuring the Navy that the band can be successfully shared without RF interference.

Army teams with Johns Hopkins to advance materials research


Sikhanda Satapathy, from DEVCOM ARL, and Prof. K.T. Ramesh, director of the Hopkins Extreme Materials Institute, will lead the research activities. To launch these projects, the partners held a joint virtual kickoff meeting Nov. 4."This collaborative agreement will enable and accelerate intelligent design of materials for extreme dynamic environments to support our Soldiers and address Army's future material needs," Satapathy said.Over the next two years, researchers will explore the use of artificial intelligence and machine learning to accelerate materials development. One of the projects is focused using artificial intelligence techniques to accelerate the processing and characterization of new materials."This The experimentally and computationally generated data will be used to train neural networks which will be used to accelerate the materials design process.Another project will incorporate machine learning of acoustic emission measurements to characterize materials deformation mechanisms. These measurements are non-trivial and require expertise in both instrumentation and data analysis."This