2018 IEEE Radar Conference (RadarConf18) 2018
DOI: 10.1109/radar.2018.8378632
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Cognitive software defined radar: A reactive approach to RFI avoidance

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Cited by 17 publications
(3 citation statements)
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References 12 publications
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“…The real-time host processes include online stochastic model learning, prediction threshold optimization, and combined prediction with waveform generation. This predictive spectrum sharing scheme builds upon the system architecture for a reactive cognitive radar described in [51]. The reactive system's host controller was modified to perform prediction and develop memory of the RF environment.…”
Section: System Implementation and Evaluationmentioning
confidence: 99%
“…The real-time host processes include online stochastic model learning, prediction threshold optimization, and combined prediction with waveform generation. This predictive spectrum sharing scheme builds upon the system architecture for a reactive cognitive radar described in [51]. The reactive system's host controller was modified to perform prediction and develop memory of the RF environment.…”
Section: System Implementation and Evaluationmentioning
confidence: 99%
“…centre frequency, pulse repetition frequency etc.) based on a variety of possible observations about the environment such as target/clutter characteristics and spectral occupancy by other radio frequency (RF) users [1][2][3][4][5][6][7]. The particular focus here is on the automated generation of physically realisable waveforms that possess spectral notches to avoid in-band interference.…”
Section: Introductionmentioning
confidence: 99%
“…SDR has also found its way into cognitive radar. Kirk et al [77] presents a cognitive radar with spectrum sensing able to select Radio-Frequency Interference (RFI) free frequency intervals. LabView, is used for signal generation and processing.…”
Section: Bibliographic Reviewmentioning
confidence: 99%