SPARSE RECONSTRUCTION FOR NEAR-FIELD MIMO RADAR IMAGING USING FAST MULTIPOLE METHODS

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2022-1-27
Miran, Emre Alp
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 typically sparse and objects of interest are located at the near-field of the antenna. More importantly, in most of these applications, the imaging system has to deal with the requirement of high quality real-time recovery from large-scale under-sampled measurement data. In this thesis, we aim to develop an efficient sparse solution method to large-scale near-field imaging problems. For this purpose, we first construct the imaging problem as a convex optimization problem and solve it using the augmented Lagrangian based reconstruction algorithms. Then, for large scale problems, we propose applying the fast multipole method (FMM) formulation in these algorithms for efficient computation of matrix-vector products. This approach avoids constructing and storing large-scale sensing matrices explicitly in memory and accelerates the reconstruction. We numerically test the effectiveness of the approach for several near-field imaging scenarios, ranging from point scatterers to extended targets (2-D/3-D). Results show that we can successfully apply FMM in the sparse reconstruction algorithms and it makes the reconstructions very efficient in terms of both computation time and memory usage.

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Citation Formats
E. A. Miran, “SPARSE RECONSTRUCTION FOR NEAR-FIELD MIMO RADAR IMAGING USING FAST MULTIPOLE METHODS,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.