Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method

Miran E. A., ÖKTEM S. F., Koc S.

IEEE ACCESS, vol.9, pp.151578-151589, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.1109/access.2021.3126472
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.151578-151589
  • Keywords: Imaging, Radar imaging, Sensors, Image reconstruction, MIMO communication, Matching pursuit algorithms, Inverse problems, Multiple-input-multiple-output radar imaging, near-field imaging, inverse problem, sparse reconstruction, fast multipole method, ERROR ANALYSIS, WAVE, EQUATIONS, ALGORITHM, SYSTEMS
  • Middle East Technical University Affiliated: Yes


Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing. In this paper, we deal with obtaining sparse solution to near- field radar imaging problems by developing efficient sparse reconstruction, which avoid storing and using large-scale sensing matrices. We demonstrate that the "fast multipole method" can be employed within sparse reconstruction algorithms to efficiently compute the sensing operator and its adjoint (backward) operator, hence improving the computation speed and memory usage, especially for large-scale 3-D imaging problems. For several near-field imaging scenarios including point scatterers and 2-D/3-D extended targets, the performances of sparse reconstruction algorithms are numerically tested in comparison with a classical solver. Furthermore, effectiveness of the fast multipole method and efficient reconstruction are illustrated in terms of memory requirement and processing time.