Improving the capabilities of cognitive radar and EW systems

被引：0

作者：

Fountain, Tim ^{[1
]}

Humbert, Leander ^{[2
]}

机构：

[1] Rohde & Schwarz, Radar & EW Global Market, Portland, OR 97124 USA

[2] Rohde & Schwarz, Munich, Germany

来源：

2023 IEEE AUTOTESTCON | 2023年

关键词：

D O I：

10.1109/AUTOTESTCON47464.2023.10296238

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this white paper we will review the challenges that mode-agile (WARM) radar and EW threat emitters pose to traditional static threat library implementations in radar and EW systems, and consider the architecture of cognitive artificial intelligence (AI) and machine learning (ML) systems that can be used to deliver effective RF countermeasures. We will discuss how a wideband RF record, simulation and playback system can be used to train the AI/ML engines and evaluate the responses and effectiveness of those countermeasures on real hardware.

引用

页数：6

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[4]

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[6]

Radlbeck Andrew, BAE Systems, Milcom 2018, track 4, proceedings

[7]

Sarma Kandarpa Kumar, IEEE Access, V8

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