Cognitive Nonlinear Radar

Report No. ARL-MR-0837
Authors: Anthony Martone, David McNamara, Gregory Mazzaro, and Abigail Hedden
Date/Pages: January 2013; 18 pages
Abstract: In this report, a unique cognitive nonlinear radar (CNR) is introduced. Research and development efforts for the CNR are currently funded by the U.S. Army Research Laboratory (ARL). The CNR adapts to (1) an increasingly cluttered electromagnetic (EM) environment, a growing problem for ground-based and airborne radar systems; (2) multiple targets; and (3) other radar, communication, and electronic systems that must operate without interfering with each other. The CNR uses a narrowband, nonlinear radar target detection methodology. This methodology has the advantage, as compared with other nonlinear radar systems that do not implement a cognitive scheme, to adapt to the radio frequency (RF) environment by intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM interference, (2) target likelihood, and (3) permissible transmit frequencies as specified by regulations and allowable by other systems operations within the environment.
Distribution: Approved for public release
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Last Update / Reviewed: January 1, 2013