In this paper a new method is introduced for path planning of an autonomous vehicle. In this method, the environment is considered cluttered and with some uncertainty sources. Thus, the state of detected object should be estimated using an optimal filter. To do so, the state distribution is assumed Gaussian. Thus the state vector is estimated by a Kalman filter at each time step. The estimation...

Robust Implementations of the Multistage Wiener Filter By John David Hiemstra The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition pr...

Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned usin...

Attribute reduction is a critical problem in the data preprocessing step with aim of minimizing redundant attributes to improve efficiency mining models. The fuzzy rough set theory considered an effective tool solve attribute directly on original decision system, without preprocessing. With current digital transformation trend, systems are larger size and updated. To change systems, number rece...