Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Electrical and Electronics Engineering, Turkey
Approval Date: 2018
Student: MEHMET ÇETİNKAYA
Supervisor: AYDAN MÜŞERREF ERKMENAbstract:
In this study, the aim is to design a respiratory motion prediction algorithm which can be used to compensate for this physiological disturbance in medical operations where respiration limits operation accuracy. For this purpose, a new Kalman filter has been developed for tracking quasi-periodic signals approximated as finite Fourier series. Instead of relying on approximations provided by Extended Kalman Filter or Unscented Kalman Filter, our filter performs the exact calculation of the mean and covariances of interest. Our results indicate that the theoretically derived mean and covariance calculations result in either comparable or better estimation performance in terms of convergence speed and output estimation error depending on the circumstances. We then employ an expectation maximization algorithm to find the maximum likelihood estimates of the process noise with known measurement noise statistics. Coupled with this, the new filter is able to track the output despite breathing irregularities. However, the degree of irregularity may cause divergence from the assumed underlying model.