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Showing result 1 - 5 of 65 swedish dissertations matching the above criteria.
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1. Multiple Model Filtering and Data Association with Application to Ground Target Tracking
Abstract : This thesis is concerned with two central parts of a tracking system, namely multiple-model filtering and data association. Multiple models are introduced to provide accurate filtering, whereas data association deals with the unknown origin of the received measurements. READ MORE
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2. Performance and Implementation Aspects of Nonlinear Filtering
Abstract : I många fall är det viktigt att kunna få ut så mycket och så bra information som möjligt ur tillgängliga mätningar. Att utvinna information om till exempel position och hastighet hos ett flygplan kallas för filtrering. I det här fallet är positionen och hastigheten exempel på tillstånd hos flygplanet, som i sin tur är ett system. READ MORE
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3. Channel Estimation and Prediction for MIMO OFDM Systems : Key Design and Performance Aspects of Kalman-based Algorithms
Abstract : Wireless broadband systems based on Orthogonal Frequency Division Multiplexing (OFDM) are being introduced to meet demands for high data transfer rates. In multiple users systems, the available bandwidth has to be shared efficiently by several users. The radio channel quality will fluctuate, or fade, as users move. READ MORE
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4. Estimation and Control of Resonant Systems with Stochastic Disturbances
Abstract : The presence of vibration is an important problem in many engineering applications. Various passive techniques have traditionally been used in order to reduce waves and vibrations, and their harmful effects. Passive techniques are, however, difficult to apply in the low frequency region. READ MORE
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5. Toward Sequential Data Assimilation for NWP Models Using Kalman Filter Tools
Abstract : The aim of the meteorological data assimilation is to provide an initial field for Numerical Weather Prediction (NWP) and to sequentially update the knowledge about it using available observations. Kalman filtering is a robust technique for the sequential estimation of the unobservable model state based on the linear regression concept. READ MORE