Military Embedded Systems

Machine learning approach could improve radar in congested environments


November 25, 2020

Emma Helfrich

Technology Editor

Military Embedded Systems

Stock image.

ADELPHI, Md. Research being conducted by the U.S. Army Combat Capabilities Development Command (DEVCOM) is focused on a new machine learning approach that could improve radar performance in congested environments.

Researchers from DEVCOM, Army Research Laboratory, and Virginia Tech have developed an automatic way for radars to operate in congested and limited-spectrum environments created by commercial 4G LTE and future 5G communications systems.

The researchers claim they examined how future Department of Defense radar systems will share the spectrum with commercial communications systems. The team used machine learning to learn the behavior of ever-changing interference in the spectrum and find clean spectrum to maximize the radar performance. Once clean spectrum is identified, waveforms can be modified to best fit into the spectrum.

This research is part of a larger defense program to implement adaptive signal processing and machine learning algorithms onto software-defined radar platforms for autonomous real-time behavior, according to officials.


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U.S. Army Research Laboratory

2800 Powder Mill Road
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