A Comparison of sparse signal recovery and approximate bayesian inference methods for sparse channel estimation

Uçar, Ayla
The concept of sparse representation is one of the central methodologies of modern signal processing and it has had significant impact on numerous application fields such as communications and imaging. Sparsity expresses the idea that the information rate of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length. With recent advances in sparse signal estimation, some new estimation techniques have emerged yielding more accurate sparse estimates than the traditional methods. The main goal of this thesis is to analyse the performance of recently proposed sparse signal estimation methods on the problem of sparse channel estimation. In this thesis, a literature survey has been conducted to examine the approaches for estimating the sparse channels, then greedy pursuit algorithms, convex relaxation and an approximate Bayesian inference method, namely expectation propagation method, are comparatively studied


Application of F-test method on model order selection and related problems
Yazar, Alper; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015)
Signal modeling is one of the important topics of signal processing area. The input signal should be modeled with a suitable mathematical model first. In statistics related disciplines, there are information theory based criteria for model order selection topic. In this thesis work, F-test based methods are proposed on model order selection and related problems. F-test is used in statistics related disciplines. However, it is not so widely used in signal processing related problems. Solution approaches for ...
On the Eigenstructure of DFT Matrices
Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2011-03-01)
The discrete Fourier transform (DFT) not only enables fast implementation of the discrete convolution operation, which is critical for the efficient processing of analog signals through digital means, but it also represents a rich and beautiful analytical structure that is interesting on its own. A typical senior-level digital signal processing (DSP) course involves a fairly detailed treatment of DFT and a list of related topics, such as circular shift, correlation, convolution operations, and the connectio...
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In this study, first, some received signal strength (RSS) based localization techniques, including maximum likelihood estimation (MLE), multidimensional scaling (MDS) and weighted least squares (WLS), are investigated and compared to each other via a simulation study within the perspective of a collaborative localization scenario. MLE using RSS measurement model, called RSS-MLE is known in the literature to be significantly biased. An important observation of this work is that the aforementioned bias can be...
Discrete wavelet transform based shift invariant analysis scheme for transient sound signals
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Discrete wavelet transform (DWT) has gained widespread recognition and popularity in signal processing due to its ability to underline and represent time-varying spectral properties of many transient and other nonstationary signals. However, DWT is a shift-variant transform. This shift-variance is a major problem with the use of DWT for transient signal analysis and pattern recognition applications. A number of modified forms of DWT have been investigated in recent years that provide approximate shift-invar...
Signal reconstruction from nonuniform samples
Serdaroğlu, Bülent; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2005)
Sampling and reconstruction is used as a fundamental signal processing operation since the history of signal theory. Classically uniform sampling is treated so that the resulting mathematics is simple. However there are various instances that nonuniform sampling and reconstruction of signals from their nonuniform samples are required. There exist two broad classes of reconstruction methods. They are the reconstruction according to a deterministic, and according to a stochastic model. In this thesis, the mos...
Citation Formats
A. Uçar, “A Comparison of sparse signal recovery and approximate bayesian inference methods for sparse channel estimation,” M.S. - Master of Science, Middle East Technical University, 2015.