Cognitive Nonlinear Radar

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA570993

Entities

People

  • Abigail Hedden
  • Anthony F. Martone
  • David Mcnamara
  • Gregory J. Mazzaro

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Detection
  • Detectors
  • Environment
  • Frequency
  • Frequency Bands
  • Genetic Algorithms
  • Machine Learning
  • Military Research
  • Multiobjective Optimization
  • Power Spectra
  • Radar
  • Radio Frequency
  • Supervised Machine Learning
  • Target Detection
  • Targets

Fields of Study

  • Engineering

Readers

  • Radio communications and signal processing.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

  • Microelectronics