Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Jacob A. Kovarskiy, Benjamin H. Kirk, Anthony F. Martone, Ram M. Narayanan, Kelly D. Sherbondy

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The growing demand for radio frequency (RF) spectrum access poses new challenges for next-generation radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system, which predicts and avoids RF interference (RFI) in real time. The system applies a cognitive perception-action cycle that senses RFI, learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real time and share the spectrum with emitters, such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RFI on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases.

Original languageEnglish (US)
Article number9226487
Pages (from-to)690-705
Number of pages16
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume57
Issue number1
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar'. Together they form a unique fingerprint.

Cite this