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

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.


Miran, Emre Alp; Koç, Seyit Sencer; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2022-1-27)
Multiple-input-multiple-output (MIMO) radar is an advanced radar technique, where spatially distributed transmitting and receiving sub-arrays operate sequentially or simultaneously. In this technique, each antenna may transmit either the same or different waveforms, and this leads to better spatial resolution when compared to conventional phased array radar. Therefore, MIMO radar has been extensively used in imaging applications in the last two decades. In all these applications, the imaged scene is typical...
Algebraic spectral moments based moving clutter parameter estimation and clutter suppression
Oktar, Onur; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2014)
In many modern radar systems, it is desired to detect the presence of targets in the interference which includes clutter and noise. Various signal processing techniques are proposed to effectively suppress the clutter and increase the signal to interference ratio. To achieve optimum suppression, radar system must know clutter characteristics and process the radar echoes based on these characteristics. For ground radars, the clutter environment characteristics are relatively stable and predictable. These cha...
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True time delay networks are one of the most critical structures of wideband phased-array antenna systems which are frequently used in self-protection and electronic warfare applications. In order to direct the main beam of a wideband phased-array antenna to the desired direction; phase values, which are linearly dependent to frequency, are essential. Due to the phase characteristics of the true-time delay networks, beam squint problems for broadband phased array systems are minimized. In this thesis, diffe...
The Fast Multipole Method for Sparse Solution of Linear Inverse Scattering Problems
Miran, Emre Alp; Koç, Seyit Sencer (2018-11-02)
The sparse solution for the linear inverse problems provide useful results for many fundamental engineering applications such as radar imaging. The studies in the literature has shown that the computational methods for the sparse solution tend to be slow as the imaging problem gets electromagnetically large, therefore the image reconstruction gets harder for the existing computational resources. The fast multipole method (FMM) can reduce the number of operations and the memory requirement for the solution o...
Deep learning-based reconstruction methods for near-field MIMO radar imaging
Manisalı, İrfan; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2022-6-29)
Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, and surveillance. These computational imaging systems reconstruct the three-dimensional scene reflectivity distribution from the radar data. Hence their imaging performance largely depends on the image reconstruction method. The analytical reconstruction methods suffer from either low image quality or high computational cost. In fact, sparsity-bas...
Citation Formats
E. A. Miran, S. F. Öktem, and S. S. Koç, “Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method,” IEEE ACCESS, vol. 9, pp. 151578–151589, 2021, Accessed: 00, 2021. [Online]. Available: