Cognitive Software-Defined Radar: Evaluation of Target Detection with RFI Avoidance

Kirk, Benjamin H.; Kozy, Mark; Gallagher, Kyle A.; Narayanan, Ram M.; Buehrer, R. Michael; Martone, Anthony F.; Sherbondy, Kelly D.

doi:10.1109/radar.2019.8835498

Cited by 11 publications

(2 citation statements)

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“…This is easiest shown via example. Consider the spectrally agile cognitive SDRadar system presented in [4] operating over a 100 MHz frequency band with another intermittent RF signal. In this example, the radar desires to operate from

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to 0 MHz, but another RF signal is accessing that portion of the band causing interference and performance degradation.…”

Section: Distortion and Proposed Solutionmentioning

confidence: 99%

“…The need for a cognitive radar system to adapt its transmit waveform in real time to avoid RF interference (RFI) has been underscored [2,3]. Cognitive radar systems employing RFI avoidance via centre frequency (CF) and bandwidth adaptation have been shown to significantly improve the signal-to-interference-plus-noise ratio while operating in-band with other RF sources by mitigating mutual interference [4].…”

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confidence: 99%

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Mitigation of target distortion in pulse‐agile sensors via Richardson–Lucy deconvolution

et al. 2019

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Pulse-agile radar systems are becoming more prevalent as the demand for adaptive and cognitive systems increases. This focus is motivated by the need for interference avoidance and spectrum sharing. Pulse agility within a coherent processing interval (CPI) or intra-CPI adaption has been shown to cause distortion, which will negatively impact the radar's performance. This problem can be framed in the context of image processing such that an ideal range-Doppler image is corrupted by some point spread function. Deconvolution is then applied to remove this distortion and improve radar performance while maintaining the ability to use computationally efficient fast Fourier transform-based processing.

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