Parçacık Süzgeci ile Doğrusal olmayan Modellerde Gauss Gürültüsü Parametreleri Kestirimi

Particle filters, which has been designed to find a solution to the problem of state estimation in highly nonlinear systems has been applied to many areas where Kalman filter or its variant are not successful. The success of particle filters also relies on prior knowledge of the model parameters. But in many applications it might not be easy to know or guess the all parameters of the model priori. In this study, it is aimed to make the particle filter adaptive by estimating the unknown noise parameters in Bayesian framework. The proposed method is efficient such that it uses the marginalization approach as in the marginalized particle filters and the conjugate priors are used in order to obtain analytical substructures.


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...
Design of attitude estimation algorithms for inertial sensors only measurement scenarios
Candan, Batu; Söken, Halil Ersin; Department of Aerospace Engineering (2022-3-24)
This thesis proposes four novel robust Kalman filter algorithms for attitude estimation using only the measurements of an inertial measurement unit. Efficiency and optimality of the Kalman filter based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the inertial measurement unit-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain ro...
A General framework for adaptive radar detection based on fast and slow-time preprocessing
Saraç, Uğur Berkay; Güvensen, Gökhan Muzzaffer.; Department of Electrical and Electronics Engineering (2019)
This thesis is about the design of an adaptive radar detector under heterogeneous clutter environment using a small number of secondary data, which is at the same time robust to Doppler mismatch. To this end, the observations taken from heterogeneous clutter environment are first processed with a specially designed fast-time preprocessing matrix, cleansing the target contamination in the secondary range cells. Using these clean secondary data, the covariance matrix of the clutter is estimated via the parame...
Reducing computational demand of multi-state constraint kalman filter in visual-inertial odometry applications
Eyice, Kerem; Çiloğlu, Tolga; Department of Electrical and Electronics Engineering (2019)
The aim of this study is to reduce the computational load required by Multi-State Constraint Kalman Filter in visual-inertial odometry applications while maintaining the accuracy of the localization solution. In order to accomplish this, a keyframe-based pose selection mechanism is proposed. The proposed method fuses visual measurements with inertial measurements in order to estimate the kinematics of the platform. The contribution of this study is to reduce computational demand of the filtering operations ...
An adaptive PHD filter for tracking with unknown sensor characteristics
Zhao, Yuxin; Yin, Feng; Gunnarsson, Fredrik; Amirijoo, Mehdi; Özkan, Emre; Gustafsson, Fredrik (2013-07-09)
The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector w...
Citation Formats
E. Özkan, “Parçacık Süzgeci ile Doğrusal olmayan Modellerde Gauss Gürültüsü Parametreleri Kestirimi,” 2011, Accessed: 00, 2020. [Online]. Available: