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

Amrita Mishra | Zendy; Vini Gupta | Zendy; Saumya Dwivedi | Zendy; Aditya K. Jagannatham | Zendy; Pramod K. Varshney | Zendy

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

Author(s) -

Amrita Mishra,

Vini Gupta,

Saumya Dwivedi,

Aditya K. Jagannatham,

Pramod K. Varshney

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2880242

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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