Successive QCQP Refinement for MIMO Radar Waveform Design under Practical Constraints

Omar Aldayel, Vishal Monga, Muralidhar Rangaswamy

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

Abstract

The authors address the problem of designing a waveform for multiple-input multiple-output (MIMO) radar under the important practical constraints of constant modulus and waveform similarity. Incorporating these constraints in an analytically tractable manner is a longstanding open challenge. This is due to the fact that the optimization problem that results from signal-To-interference-plus-noise ratio (SINR) maximization subject to these constraints is a hard non-convex problem. The authors develop a new analytical approach that involves solving a sequence of convex quadratically constrained quadratic programing (QCQP) problems, which they prove converges to a sub-optimal solution. Because an improvement in SINR results via solving each problem in the sequence, they call the method Successive QCQP Refinement (SQR). Furthermore, the proposed SQR method can be easily extended to incorporate emerging requirements of spectral coexistence, as shown briefly in this paper. The authors evaluate SQR against other candidate techniques with respect to SINR performance, beam pattern, and pulse compression properties in a variety of scenarios. Results show that SQR outperforms state-of-The-Art methods that also employ constant modulus and/or similarity constraints while being computationally less burdensome.

Original languageEnglish (US)
Article number7450660
Pages (from-to)3760-3774
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume64
Issue number14
DOIs
StatePublished - Jul 15 2016

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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