Computationally simple MMSE (A-optimal) Adaptive Beam-pattern Design for MIMO Active Sensing Systems via a Linear-Gaussian Approximation

Steven Herbert, James R. Hopgood, Bernard Mulgrew

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper presents an approximate minimum mean squared error (MMSE) adaptive beam-pattern design (ABD) method for MIMO active sensing systems. The proposed approximate MMSE ABD method leverages the physics of the MIMO arrays to provide a linear-Gaussian approximation that is specific to MIMO active sensing systems, and yields a computationally simple optimization problem. Computational complexity analysis confirms this theoretical reduction in the number of floating-point operations required, most notably that evaluation of the proposed approximate optimization cost function grows polynomially with the number of targets being tracked, whereas for evaluation of the exact cost the growth is exponential. Additionally, numerical results indicate that, even for a simple scenario with a single target being tracked, the proposed approximate MMSE ABD method does indeed reduce the mean squared error of target parameter estimation compared to the nonadaptive case, with a reduction in computation time of four orders of magnitude compared to exact MMSE ABD.
Original languageEnglish
Pages (from-to)4935 - 4945
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number18
Early online date9 Aug 2018
Publication statusPublished - 15 Sept 2018

Keywords / Materials (for Non-textual outputs)

  • Adaptive waveform design
  • adaptive beampattern design
  • adaptive beamforming
  • minimum mean squared error
  • active sensing
  • MIMO
  • radar
  • Bayesian
  • optimal design
  • adaptive beam-forming


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