Search for dissertations about: "Covariance fitting"
Showing result 1 - 5 of 13 swedish dissertations containing the words Covariance fitting.
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1. Spectral Analysis of Nonuniformly Sampled Data and Applications
Abstract : Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. READ MORE
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2. Parameter Estimation - in sparsity we trust
Abstract : This thesis is based on nine papers, all concerned with parameter estimation. The thesis aims at solving problems related to real-world applications such as spectroscopy, DNA sequencing, and audio processing, using sparse modeling heuristics. READ MORE
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3. Subspace-Based Parameter Estimation Problems in Signal Processing
Abstract : The effects of multipath-induced angular spread and non-zero band-width on narrow-band direction-of-arrival (DOA) estimation are investigated. In both cases expressions for the resulting estimation error are developed for the MUSIC, ESPRIT and WSF DOA estimators. READ MORE
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4. On Reduced Rank Linear Regression and Direction Estimation in Unknown Colored Noise Fields
Abstract : Two estimation problems are treated in this thesis. Estimators are suggested and the asymptotical properties of the estimates are investigated analytically. Numerical simulations are used to assess small-sample performance. In addition, performance bounds are calculated. READ MORE
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5. Parameter Estimation for Multisensor Signal Processing : Reduced Rank Regression, Array Processing and MIMO Communications
Abstract : This thesis deals with three estimation problems motivated by spatial signal processing using arrays of sensors. All three problems are approached using tools from estimation theory, including asymptotical analysis of performance and Cramér-Rao lower bound; Monte Carlo methods are used to evaluate small sample performance. READ MORE