Computationally efficient waveform design in spectrally dense environment

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The problem of unimodular radar waveform design with similarity constraint in spectrally dense environment is considered. The corresponding optimization problem is nonconvex and additionally large-scale. Indeed, the waveform length can be of several thousands and the waveform has to be designed in every coherent processing interval. Since the problem is nonconvex, the majorization-minimization (MaMi) method is used for addressing it, and since it is large-scale, the alternating direction method of multipliers (ADMM) is adopted. Thus, we develop a computationally efficient approach for solving the problem by using MaMi with proper design of majorization function together with alternating ADMM with newly proposed computationally efficient proximal projections. We evaluate the computational cost of the proposed algorithm and show its fast convergence unmatched by existing approaches in terms of simulations.


Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Publication statusPublished - 27 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Sensor Array and Multichannel Signal Processing Workshop - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018
Conference number: 10

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Print)1551-2282
ISSN (Electronic)2151-870X


WorkshopIEEE Sensor Array and Multichannel Signal Processing Workshop
Abbreviated titleSAM
CountryUnited Kingdom

ID: 28402613