Gürültü altuzayı tabanlı DOA kestirim algoritmalarının CPU ve GPU üzerinde parallelleştirilmesi

Direction-of-Arrival (DOA) estimation is known as an active research area, and it is studied under array signal processing. The algorithms in this area are widely used in various applications such as sonar, search-and-rescue, navigation, and geolocation. However, achieving a real-time system performance is sometimes a challenging task for these algorithms. In this thesis, four noise subspace-based DOA estimation algorithms (PHD, MUSIC, EV, and MN) were considered and implemented in MATLAB, C/C++, and CUDA. MATLAB implementations were mainly used for theoretical and numerical analyses. Whereas, C/C++ implementations were initially used for constructing the parallelization structure and they were realized in two versions working serially and in parallel manner (via OpenMP). Within the scope of theoretical analysis, these algorithms were compared with each other in terms of DOA estimation accuracy and effects of change in different parameters (e.g., array geometry, array aperture, SNR level, etc.) on the accuracy were observed. On the other hand, in terms of implementation-based experiments, all the codes in MATLAB, C/C++, and CUDA were evaluated from both numerical and performance viewpoints. The general numerical validation of C/C++ and CUDA codes was realized against ground-truth MATLAB codes. After the initial assessment of the CUDA code performance, some GPU-based optimizations were applied and the corresponding performance improvements were evaluated. The CUDA code performance was benchmarked on the PC platforms with different system configurations. Consequently, a considerable speedup was achieved for CUDA codes compared to baseline multi-threaded C/C++ codes.