Search for dissertations about: "Subspace-based parameter estimation problems in signal processing"

Found 3 swedish dissertations containing the words Subspace-based parameter estimation problems in signal processing.

2. 1. Subspace-Based Parameter Estimation Problems in Signal Processing

   Author : Joakim Sorelius; Torsten Söderström; Peter Stoica; Uppsala universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; sensor array processing; parameter estimation; spread spectrum communication; direct sequence CDMA; order estimation; subspace fitting; blind identification; system identification; Signal Processing; Signalbehandling;

   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

4. 2. Estimation Using Low Rank Signal Models

   Author : Kaushik Mahata; Torsten Söderström; Mats Viberg; Uppsala universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; parameter estimation; separable least squares; statistical analysis; viscoelasticity; subspace algorithms; spectrum estimation; Signal processing; Signalbehandling;

   Abstract : Designing estimators based on low rank signal models is a common practice in signal processing. Some of these estimators are designed to use a single low rank snapshot vector, while others employ multiple snapshots. This dissertation deals with both these cases in different contexts. READ MORE

5. 3. On performance analysis of subspace methods in system identification and sensor array processing

   Author : Magnus Jansson; Bo Wahlberg; Mats Molander; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; subspace methods; system identification; state-space models; linear regression; sensor array processing; performance analysis; EM algorithm; minimum redundancy array;

   Abstract : This thesis addresses the issue of performance analysis of subspace-based parameter estimation methods in two different applications, namely system identification and sensor array processing.  The objective is to study the quality of the estimated models as the amount of data increases, and to suggest improvements and give user guidelines. READ MORE