Successive QCQP Refinement for MIMO Radar Waveform Design under Practical Constraints

Omar Aldayel, Vishal Monga, Muralidhar Rangaswamy

    Research output: Contribution to journalArticle

    25 Citations (Scopus)

    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

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    Radar
    Pulse compression

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

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    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.",
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    Successive QCQP Refinement for MIMO Radar Waveform Design under Practical Constraints. / Aldayel, Omar; Monga, Vishal; Rangaswamy, Muralidhar.

    In: IEEE Transactions on Signal Processing, Vol. 64, No. 14, 7450660, 15.07.2016, p. 3760-3774.

    Research output: Contribution to journalArticle

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