SQR: Successive QCQP refinement for MIMO radar waveform design under practical constraints

Omar Aldayel, Vishal Monga, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We address the problem of designing a waveform for Multiple-Input Multiple-Output (MIMO) radar under important practical constraints, namely the constant modulus and the waveform similarity constraints. Incorporating these constraints in an analytically tractable manner continues to be longstanding open challenge. This is because the optimization problem that results from Signal to Interference plus Noise Ratio (SINR) maximization subject to these constraints is a hard non-convex problem. We develop a new analytical approach that involves solving a sequence of convex Quadratic Constrained Quadratic Programing (QCQP) problems, which we prove converges to a sub-optimal solution. We call the method Successive QCQP Refinement (SQR). We evaluate SQR against state of the art in its SINR performance for a practical scenario and show that it outperforms existing methods without incurring a significant computational burden.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages85-89
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove
Period11/8/1511/11/15

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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