Maciej Niedźwiecki - Publikacje - MOST Wiedzy

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Rok 2021
Rok 2022
  • Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters
    Publikacja

    - SIGNAL PROCESSING - Rok 2022

    We consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identi- fication of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identi- fying time-varying systems....

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  • Adaptive Identification of Underwater Acoustic Channel with a Mix of Static and Time-Varying Parameters
    Publikacja

    - Rok 2022

    We consider the problem of identification of communication channels with a mix of static and time-varying parameters. Such scenarios are typical, among others, in underwater acoustics. In this paper, we further develop adaptive algorithms built on the local basis function (LBF) principle resulting in excellent performance when identifying time-varying systems. The main drawback of an LBF algorithm is its high complexity. The subsequently...

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  • Finite-window RLS algorithms
    Publikacja

    - SIGNAL PROCESSING - Rok 2022

    Two recursive least-squares (RLS) adaptive filtering algorithms are most often used in practice, the exponential and sliding (rectangular) window RLS algorithms. This popularity is mainly due to existence of low-complexity versions of these algorithms. However, these two windows are not always the best choice for identification of fast time-varying systems, when the identification performance is most important. In this paper, we...

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  • Optimally regularized local basis function approach to identification of time-varying systems
    Publikacja

    Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state of the art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately...

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Rok 2023
Rok 2024

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