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.


Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
Miran, Emre A.; Öktem, Sevinç Figen; Koç, Seyit Sencer (2021-01-01)
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 demonstr...
Outlier robust filters and their multiple model extensions
Şahin Bozgan, İlknur; Özkan, Emre; Department of Electrical and Electronics Engineering (2019)
Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on noisy measurements, has been successfully employed in many applications such as navigation, control, signal processing and target tracking. It is the optimum Bayesian filter in terms of mean square error (MSE) for linear Gaussian state-space models (SSMs). However, in many real world applications, the performance of KF degrades due to the presence of outliers in noises. Motivated by this problem, several algori...
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...
A Survey on s-band phase shifters
Kızıltaş, Bilgin; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2013)
Phase shifters are essential components of beam directing in electronically scanned phased array radar transmitters and receivers that are used in electronic warfare applications for surveillance and self-protection reasons. In this thesis, initially, fundamentals of phase shifters and various phase shifter topologies are introduced. Afterwards, two-stage all pass filter based phase shifter, eight-section loaded-line phase shifter, aperture coupler based phase shifter and double shunt stub phase shifter cir...
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