Neural Networks & Machine Learning in Cognitive Radar

被引：0

作者：

Smith, Graeme E. ^{[2
]}

Gurburt, Sevgi Z. ^{[3
]}

Bruggenwirth, Stefan ^{[4
]}

John-Baptiste, Peter ^{[1
]}

机构：

[1] Johns Hopkins Appl Phys Lab, Laurel, MD USA

[2] Univ Alabama, Tuscaloosa, AL 35487 USA

[3] Fraunhofer FHR, Wachtberg, Germany

[4] Ohio State Univ, Columbus, OH 43210 USA

来源：

2020 IEEE RADAR CONFERENCE (RADARCONF20) | 2020年

关键词：

cognitive radar; neural networks; machine learning;

D O I：

10.1109/radarconf2043947.2020.9266670

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper reports on how neural networks and machine learning can support the development of cognitive radar systems. We discuss the aspects of cognition that can be supported by neural networks, review the recent literature on the use of neural networks for radar and review the significant challenges to implementation. The paper concludes with an example where a neural network, trained using reinforcement learning, generates radar waveforms containing a 26 dB notch in the power spectral density. The notch location is specified using a spectral mask that is the input to the neural network.

引用

页数：6

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