Spectral prediction and notching of RF emitters for cognitive radar coexistence

Jacob A. Kovarskiy, Jonathan W. Owen, Ram M. Narayanan, Shannon D. Blunt, Anthony F. Martone, Kelly D. Sherbondy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The increasing demand for radio frequency (RF) spectrum requires radars to coexist with interfering RF emitters. Innovations in waveform design paired with dynamic spectrum access techniques allow for efficient spectrum sharing for radar. This work evaluates a recently developed real-time implementation of spectrum sharing cognitive radar using commercial-of-the-shelf (COTS) hardware. Prediction of coexisting RF emitter frequencies informs the design of spectrally notched FM noise waveforms on transmit. Waveform notches are optimized on a pulse-to-pulse basis while accounting for the zero-order hold model inherent to lower fidelity digital-to-analog converters to ensure desired reconstruction. The radar system employs cognition to learn and predict RF emitter activity via a stochastic model-based approach. Initially, passive spectrum observations are used to estimate a stochastic model which is then exploited to predict the likelihood of future RF activity. The benefits and limitations of this sense-predict-and-notch (SPAN) approach are evaluated using a set of synthetic interference scenarios in real-time.

Original languageEnglish (US)
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781728168128
DOIs
StatePublished - Apr 2020
Event2020 IEEE International Radar Conference, RADAR 2020 - Washington, United States
Duration: Apr 28 2020Apr 30 2020

Publication series

Name2020 IEEE International Radar Conference, RADAR 2020

Conference

Conference2020 IEEE International Radar Conference, RADAR 2020
CountryUnited States
CityWashington
Period4/28/204/30/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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