Fast Frequency-Agile Real-Time Optimization of High-Power Tuning Network for Cognitive Radar Applications

Alcala-Medel, Jose; Egbert, Austin; Calabrese, Caleb; Dockendorf, Angelique; Baylis, Charles; Shaffer, Garrett; Semnani, Abbas; Peroulis, Dimitrios; Viveiros, Edward; Gallagher, Kyle A.; Martone, Anthony F.

doi:10.1109/mwsym.2019.8701008

Cited by 14 publications

(1 citation statement)

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“…Some previous works consider cognitive radars which adapt waveform parameters to optimize target tracking and recognition [19], [20], [21]. Others explore cognitive radar aspects such as automatic target recognition and RF hardware adaptation [22], [23], [24]. In particular, this work applies the cognitive radar concept to spectrum sharing.…”

Section: A Cognition For Spectrum Sharingmentioning

confidence: 99%

Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Kovarskiy

Kirk

Martone

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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The growing demand for radio frequency (RF) spectrum access poses new challenges for nextgeneration 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 in real-time. The system applies a cognitive perceptionaction cycle which senses RF interference (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 RF interference 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.

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