Improvements to sparse signal processing in compressive sensing and other methods.
Date of Issue2012
School of Electrical and Electronic Engineering
Centre for Signal Processing
Compressive Sensing (CS), as a newly developed branch of sparse signal processing and representation approaches, has quickly found various applications in a large number of research topics in modern digital signal processing area. This work is devoted to investigating some effective CS and sparse signal processing schemes and approaches, and also to adapting the sparse signal processing idea and CS framework to some specific applications such as object-based surveillance video compression. In compressive sensing, the sampling strategy and reconstruction algorithms are two major components. In this thesis, we first investigate the optimization of the sampling matrix for effective CS performance. An optimizing method, by using a simple polynomial shrink function and a detector to estimate the convergence to stop the iterations early, is proposed to provide better CS performance. A number of experimental simulations are presented to demonstrate the optimized measurement matrix’s effectiveness.
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing