Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems

Amrita Mishra, Vini Gupta, Saumya Dwivedi, Aditya K. Jagannatham, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown target parameters. First, the SBL framework is developed for a single measurement vector setting with an underlying sparse target reflectivity parameter vector. This is subsequently extended to scenarios with multiple observation snapshots considering uncorrelated as well as correlated target reflectivity parameters. Variants are also proposed for challenging scenarios considering the presence of ground clutter. Cramér-Rao bounds are derived for the reflectivity, Doppler, and range estimates to comprehensively characterize the performance of the proposed estimation schemes. A joint parameter estimation and imaging scheme is developed based on a Taylor series expansion of the MIMO radar dictionary matrix. Simulation results demonstrate enhanced imaging and estimation accuracy of the proposed SBL schemes in comparison with the existing techniques for MIMO radar systems.

Original languageEnglish (US)
Article number8529199
Pages (from-to)68545-68559
Number of pages15
JournalIEEE Access
StatePublished - 2018


  • Cramér-Rao bound
  • Monostatic MIMO radar
  • parameter estimation
  • sparse Bayesian learning
  • target imaging

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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