MIMO radar beampattern design by using Phased-Costas waveforms with PAR constraints employing a generalized ambiguity function

Celik O. O., TUNCER T. E.

Digital Signal Processing: A Review Journal, vol.135, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 135
  • Publication Date: 2023
  • Doi Number: 10.1016/j.dsp.2023.103948
  • Journal Name: Digital Signal Processing: A Review Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Keywords: MIMO radar, Transmit beampattern, ADMM, Space-time coding, Generalized ambiguity function, Peak to average ratio (PAR), PROBING SIGNAL-DESIGN, AVERAGE-POWER RATIO, OPTIMIZATION, PERFORMANCE, DIVERSITY, SYSTEMS, PEAK
  • Middle East Technical University Affiliated: Yes


© 2023 Elsevier Inc.Multiple-input multiple-output (MIMO) radars have attracted interest due to some important advantages over Phased-array radars. The diversity that comes with multiple waveforms is used to maximize the power in the vicinity of targets and minimize the cross correlation of waveforms reflected from multiple targets for predefined angles. In this study, a novel approach is presented to overcome the limitations of current beamformer design techniques in terms of bandwidth, auto/cross (spatial) correlations, range-doppler resolutions, peak-to-average ratio (PAR) and mean squared error (MSE) for desired beamforming pattern. A generalized ambiguity function formulation is presented which employs all the beamformer design parameters in a mathematically tractable simple manner suitable for constrained optimization. We present Phased-Costas coded waveforms to generate desired beampatterns while minimizing range-doppler sidelobes and target cross-correlations with limited bandwidth. Beamformer design problem is casted as a constrained optimization which includes nonconvex PAR constraints. The resulting NP hard problem with nonconvex objective function is converted to a bi-convex form and the PAR constraints are converted as convex constraints. The final problem is efficiently solved by using Alternating Direction Method of Multipliers (ADMM) technique in polynomial time. The beamformer solution is shown to achieve PAR=1 with very little degradation compared to the unconstrained problem making the ultimate choice for MIMO beamforming applications. Several good features of the designed beamformer are shown through simulations and compared to the known methods.