7,302 research outputs found
Characterising poroelastic materials in the ultrasonic range - A Bayesian approach
Acoustic fields scattered by poroelastic materials contain key information
about the materials' pore structure and elastic properties. Therefore, such
materials are often characterised with inverse methods that use acoustic
measurements. However, it has been shown that results from many existing
inverse characterisation methods agree poorly. One reason is that inverse
methods are typically sensitive to even small uncertainties in a measurement
setup, but these uncertainties are difficult to model and hence often
neglected. In this paper, we study characterising poroelastic materials in the
Bayesian framework, where measurement uncertainties can be taken into account,
and which allows us to quantify uncertainty in the results. Using the finite
element method, we simulate measurements where ultrasonic waves are incident on
a water-saturated poroelastic material in normal and oblique angles. We
consider uncertainties in the incidence angle and level of measurement noise,
and then explore the solution of the Bayesian inverse problem, the posterior
density, with an adaptive parallel tempering Markov chain Monte Carlo
algorithm. Results show that both the elastic and pore structure parameters can
be feasibly estimated from ultrasonic measurements.Comment: Published in JSV. https://doi.org/10.1016/j.jsv.2019.05.02
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Unscented Orientation Estimation Based on the Bingham Distribution
Orientation estimation for 3D objects is a common problem that is usually
tackled with traditional nonlinear filtering techniques such as the extended
Kalman filter (EKF) or the unscented Kalman filter (UKF). Most of these
techniques assume Gaussian distributions to account for system noise and
uncertain measurements. This distributional assumption does not consider the
periodic nature of pose and orientation uncertainty. We propose a filter that
considers the periodicity of the orientation estimation problem in its
distributional assumption. This is achieved by making use of the Bingham
distribution, which is defined on the hypersphere and thus inherently more
suitable to periodic problems. Furthermore, handling of non-trivial system
functions is done using deterministic sampling in an efficient way. A
deterministic sampling scheme reminiscent of the UKF is proposed for the
nonlinear manifold of orientations. It is the first deterministic sampling
scheme that truly reflects the nonlinear manifold of the orientation
The Ensemble Kalman Filter: A Signal Processing Perspective
The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of
the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and
non-Gaussian state estimation problems. Its ability to handle state dimensions
in the order of millions has made the EnKF a popular algorithm in different
geoscientific disciplines. Despite a similarly vital need for scalable
algorithms in signal processing, e.g., to make sense of the ever increasing
amount of sensor data, the EnKF is hardly discussed in our field.
This self-contained review paper is aimed at signal processing researchers
and provides all the knowledge to get started with the EnKF. The algorithm is
derived in a KF framework, without the often encountered geoscientific
terminology. Algorithmic challenges and required extensions of the EnKF are
provided, as well as relations to sigma-point KF and particle filters. The
relevant EnKF literature is summarized in an extensive survey and unique
simulation examples, including popular benchmark problems, complement the
theory with practical insights. The signal processing perspective highlights
new directions of research and facilitates the exchange of potentially
beneficial ideas, both for the EnKF and high-dimensional nonlinear and
non-Gaussian filtering in general
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