Sökning: "State-space filter"

Visar resultat 1 - 5 av 29 avhandlingar innehållade orden State-space filter.

  1. 1. On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space Models

    Detta är en avhandling från Lund University

    Författare :Jimmy Olsson; Lunds universitet.; Lund University.; [2007]
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; actuarial mathematics; programming; operations research; Statistics; Matematik; Mathematics; state space models; smoothing; sequential Monte Carlo; particle filter; EM algorithm; maximum likelihood; consistency; Asymptotic normality; Statistik; operationsanalys; programmering; aktuariematematik;

    Sammanfattning : This thesis is based on four papers (A-D) treating filtering, smoothing, and maximum likelihood (ML) estimation in general state space models using stochastic particle filters (also referred to as sequential Monte Carlo (SMC) methods). The aim of Paper A is to study the bias of Monte Carlo integration estimates produced by the so-called bootstrap particle filter. LÄS MER

  2. 2. Acoustic Sound Source Localisation and Tracking in Indoor Environments

    Detta är en avhandling från Karlskrona : Blekinge Institute of Technology

    Författare :Anders Johansson; Blekinge Tekniska Högskola.; [2008]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Room acoustics; Speaker localisation; Tracking; State-space filter; Sequential Monte Carlo method; Particle filter;

    Sammanfattning : With advances in micro-electronic complexity and fabrication, sophisticated algorithms for source localisation and tracking can now be deployed in cost sensitive appliances for both consumer and commercial markets. As a result, such algorithms are becoming ubiquitous elements of contemporary communication, robotics and surveillance systems. LÄS MER

  3. 3. Recursive black-box identification of nonlinear state-space ODE models

    Detta är en avhandling från Uppsala University

    Författare :Linda Brus; Uppsala universitet.; Uppsala universitet.; [2006]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Elektroteknik med inriktning mot reglerteknik; Electrical Engineering with specialization in Automatic Control;

    Sammanfattning : Nonlinear system identification methods is a topic that has been gaining interest over the last years. One reason is the many application areas in controller design and system development. However, the problem of modeling nonlinear systems is complex and finding a general method that can be used for many different applications is difficult. LÄS MER

  4. 4. Identification and Synthesis of Components for Vibration Transfer Path Analysis

    Detta är en avhandling från Uppsala University

    Författare :Per Sjövall; [2007]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Sensor placement; Indirect sensing; Vibration control; System identification; Experimental methods; Substructuring; Kalman filter; Transfer path analysis; State-space models;

    Sammanfattning : Transmission of structure-borne vibrations in built-up structures, e.g. vehicles such as aircraft, automobiles and trains, is addressed. Methodologies for modeling, analysis, prediction and reduction of vibrations are developed. LÄS MER

  5. 5. On particle-based online smoothing and parameter inference in general state-space models

    Detta är en avhandling från Stockholm : KTH Royal Institute of Technology

    Författare :Johan Westerborn; KTH.; [2017]
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics;

    Sammanfattning : This thesis consists of 4 papers, presented in Paper A-D, on particle- based online smoothing and parameter inference in general state-space hidden Markov models.In Paper A a novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently performing online approxima- tion of smoothed expectations of additive state functionals in general hidden Markov models, is presented. LÄS MER