Chaotic Phase-Coded Waveforms Based on Memristor Neural Network for MIMO Radar Applications
Authors: 
Yadong Wang, Wen HuAuthors Info & Claims
Yadong Wang, Wen HuAuthors Info & ClaimsPages 134 - 139
Abstract
Waveform design plays a key role in the application of multiple-input multiple-output (MIMO) radar, radar anti-jamming, cluster system detection and reduction of radar signal interception probability. In this paper, a memristor neural network dynamic system is constructed based on the function fitting characteristics of the neural network, and the parameters of the dynamic system are optimized for the application characteristics of the waveform to generate an arbitrary sequence that meets the design conditions. The dynamic behavior and output waveform characteristics of the dynamic system constructed by the memristor neural network are analyzed, and compare the dynamic system construction method based on Taylor expansion function fitting characteristics. The results show the advantages of the proposed method in the aspects of algorithm complexity, computation and chaos.
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Published In
February 2021
342 pages
ISBN:9781450389174
DOI:10.1145/3456415
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Published: 06 June 2021
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ICCBN 2021
ICCBN 2021: 2021 9th International Conference on Communications and Broadband Networking
February 25 - 27, 2021
Shanghai, China
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