667 research outputs found
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian
state space models (SSMs) is the intractability of estimating the system state.
Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced
more than two decades ago), provide numerical solutions to the nonlinear state
estimation problems arising in SSMs. When combined with additional
identification techniques, these algorithms provide solid solutions to the
nonlinear system identification problem. We describe two general strategies for
creating such combinations and discuss why SMC is a natural tool for
implementing these strategies.Comment: In proceedings of the 17th IFAC Symposium on System Identification
(SYSID). Added cover pag
ILAPF: Incremental Learning Assisted Particle Filtering
This paper is concerned with dynamic system state estimation based on a
series of noisy measurement with the presence of outliers. An incremental
learning assisted particle filtering (ILAPF) method is presented, which can
learn the value range of outliers incrementally during the process of particle
filtering. The learned range of outliers is then used to improve subsequent
filtering of the future state. Convergence of the outlier range estimation
procedure is indicated by extensive empirical simulations using a set of
differing outlier distribution models. The validity of the ILAPF algorithm is
evaluated by illustrative simulations, and the result shows that ILAPF is more
accurate and faster than a recently published state-ofthe-art robust particle
filter. It also shows that the incremental learning property of the ILAPF
algorithm provides an efficient way to implement transfer learning among
related state filtering tasks.Comment: 5 pages, 4 figures, conferenc
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
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