81,625 research outputs found
A New Reduction Scheme for Gaussian Sum Filters
In many signal processing applications it is required to estimate the
unobservable state of a dynamic system from its noisy measurements. For linear
dynamic systems with Gaussian Mixture (GM) noise distributions, Gaussian Sum
Filters (GSF) provide the MMSE state estimate by tracking the GM posterior.
However, since the number of the clusters of the GM posterior grows
exponentially over time, suitable reduction schemes need to be used to maintain
the size of the bank in GSF. In this work we propose a low computational
complexity reduction scheme which uses an initial state estimation to find the
active noise clusters and removes all the others. Since the performance of our
proposed method relies on the accuracy of the initial state estimation, we also
propose five methods for finding this estimation. We provide simulation results
showing that with suitable choice of the initial state estimation (based on the
shape of the noise models), our proposed reduction scheme provides better state
estimations both in terms of accuracy and precision when compared with other
reduction methods
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Context: Characterization of instrumental effects in astronomical imaging is
important in order to extract accurate physical information from the
observations. The measured image in a real optical instrument is usually
represented by the convolution of an ideal image with a Point Spread Function
(PSF). Additionally, the image acquisition process is also contaminated by
other sources of noise (read-out, photon-counting). The problem of estimating
both the PSF and a denoised image is called blind deconvolution and is
ill-posed.
Aims: We propose a blind deconvolution scheme that relies on image
regularization. Contrarily to most methods presented in the literature, our
method does not assume a parametric model of the PSF and can thus be applied to
any telescope.
Methods: Our scheme uses a wavelet analysis prior model on the image and weak
assumptions on the PSF. We use observations from a celestial transit, where the
occulting body can be assumed to be a black disk. These constraints allow us to
retain meaningful solutions for the filter and the image, eliminating trivial,
translated and interchanged solutions. Under an additive Gaussian noise
assumption, they also enforce noise canceling and avoid reconstruction
artifacts by promoting the whiteness of the residual between the blurred
observations and the cleaned data.
Results: Our method is applied to synthetic and experimental data. The PSF is
estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for
SDO/AIA using the 2012 Venus transit. Results show that the proposed
non-parametric blind deconvolution method is able to estimate the core of the
PSF with a similar quality to parametric methods proposed in the literature. We
also show that, if these parametric estimations are incorporated in the
acquisition model, the resulting PSF outperforms both the parametric and
non-parametric methods.Comment: 31 pages, 47 figure
Detection/estimation of the modulus of a vector. Application to point source detection in polarization data
Given a set of images, whose pixel values can be considered as the components
of a vector, it is interesting to estimate the modulus of such a vector in some
localised areas corresponding to a compact signal. For instance, the
detection/estimation of a polarized signal in compact sources immersed in a
background is relevant in some fields like astrophysics. We develop two
different techniques, one based on the Neyman-Pearson lemma, the Neyman-Pearson
filter (NPF), and another based on prefiltering-before-fusion, the filtered
fusion (FF), to deal with the problem of detection of the source and estimation
of the polarization given two or three images corresponding to the different
components of polarization (two for linear polarization, three including
circular polarization). For the case of linear polarization, we have performed
numerical simulations on two-dimensional patches to test these filters
following two different approaches (a blind and a non-blind detection),
considering extragalactic point sources immersed in cosmic microwave background
(CMB) and non-stationary noise with the conditions of the 70 GHz \emph{Planck}
channel. The FF outperforms the NPF, especially for low fluxes. We can detect
with the FF extragalactic sources in a high noise zone with fluxes >=
(0.42,0.36) Jy for (blind/non-blind) detection and in a low noise zone with
fluxes >= (0.22,0.18) Jy for (blind/non-blind) detection with low errors in the
estimated flux and position.Comment: 11 pages, 5 figure
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