Derecesi azaltılmış Gauss süreçleri kullanılarak genişletilmiş hedef takibi

Conventional tracking algorithms are predominantly based on point target assumption; however, this assumption is challenged as a result of the advents in sensor resolutions. Improvements on processors and rapid advances in sensor capabilities has enabled to the perception of target characteristics beyond the kinematics. Extended target tracking is the ability to learn target shapes that occupy multiple resolution cells and to track the motion of the target in a recursive framework. Gaussian process, a non-parametric method to settle the bridge between inputs and outputs of a system with tractable math, is opted for this thesis to establish the extended target tracking structure. On the other hand, computational complexity can be considered as a cost of the flexible nature of Gaussian processes. We, therefore, investigate the periodic kernel spectral approximation that relies on restating the kernel matrix with a lower rank. Following this, we perform in-depth mathematical derivations, conduct simulations in comparison with other approaches, and discuss several aspects of our extended target tracking method.