@inproceedings{44326,
  abstract     = {{Low-quality models that miss relevant dynamics lead to major challenges in modelbased
state estimation. We address this issue by simultaneously estimating the system’s states
and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,
we augment the state with the parameter vector of a linear combination containing suitable
functions that approximate the lacking dynamics. Presuming that only a few dynamical terms
are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like
sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace
distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,
the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is
applied instead. Results exhibit small estimation errors with model improvements detected by
an automated model reduction technique.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{IFAC-PapersOnLine}},
  keywords     = {{joint estimation, unscented Kalman filter, sparsity, Laplacian prior, regularized horseshoe, principal component analysis}},
  location     = {{Yokohama, Japan}},
  number       = {{2}},
  pages        = {{869--874}},
  title        = {{{Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}}},
  volume       = {{56}},
  year         = {{2023}},
}

@inproceedings{11930,
  abstract     = {{For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.}},
  author       = {{Warsitz, Ernst and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)}},
  keywords     = {{acoustic filter-and-sum beamforming, acoustic room impulses, acoustic signal processing, adaptive principal component analysis, adaptive signal processing, architectural acoustics, constrained optimization problem, cross power spectral density, deterministic algorithm, deterministic algorithms, distant-talking environments, eigenvalues and eigenfunctions, eigenvector, enhanced signal, filter-and-sum beamformer, FIR filter coefficients, FIR filter coefficients, FIR filters, gradient methods, human-machine interfaces, iterative estimation, iterative methods, largest eigenvalue, microphone signals, multichannel signal processing, optimisation, principal component analysis, spectral analysis, stochastic gradient ascent algorithm, stochastic processes}},
  pages        = {{iv/797--iv/800 Vol. 4}},
  title        = {{{Acoustic filter-and-sum beamforming by adaptive principal component analysis}}},
  doi          = {{10.1109/ICASSP.2005.1416129}},
  volume       = {{4}},
  year         = {{2005}},
}