3,252 research outputs found
Automated weighing by sequential inference in dynamic environments
We demonstrate sequential mass inference of a suspended bag of milk powder
from simulated measurements of the vertical force component at the pivot while
the bag is being filled. We compare the predictions of various sequential
inference methods both with and without a physics model to capture the system
dynamics. We find that non-augmented and augmented-state unscented Kalman
filters (UKFs) in conjunction with a physics model of a pendulum of varying
mass and length provide rapid and accurate predictions of the milk powder mass
as a function of time. The UKFs outperform the other method tested - a particle
filter. Moreover, inference methods which incorporate a physics model
outperform equivalent algorithms which do not.Comment: 5 pages, 7 figures. Copyright IEEE (2015
Challenges with bearings only tracking for missile guidance systems and how to cope with them.
This paper addresses the problem of closed loop missile guidance using bearings and target angular extent information. Comparison is performed between particle filtering methods and derivative free methods. The extent information characterizes target size and we show how this can help compensate for observability problems. We demonstrate that exploiting angular extent information improves filter estimation accuracy. The performance of the filters has been studied over a testing scenario with a static target, with respect to accuracy, sensitivity to perturbations in initial conditions and in different seeker modes (active, passive and semi-active)
Nonlinear Attitude Filtering: A Comparison Study
This paper contains a concise comparison of a number of nonlinear attitude
filtering methods that have attracted attention in the robotics and aviation
literature. With the help of previously published surveys and comparison
studies, the vast literature on the subject is narrowed down to a small pool of
competitive attitude filters. Amongst these filters is a second-order optimal
minimum-energy filter recently proposed by the authors. Easily comparable
discretized unit quaternion implementations of the selected filters are
provided. We conduct a simulation study and compare the transient behaviour and
asymptotic convergence of these filters in two scenarios with different
initialization and measurement errors inspired by applications in unmanned
aerial robotics and space flight. The second-order optimal minimum-energy
filter is shown to have the best performance of all filters, including the
industry standard multiplicative extended Kalman filter (MEKF)
3D angle-of-arrival positioning using von Mises-Fisher distribution
We propose modeling an angle-of-arrival (AOA) positioning measurement as a
von Mises-Fisher (VMF) distributed unit vector instead of the conventional
normally distributed azimuth and elevation measurements. Describing the
2-dimensional AOA measurement with three numbers removes discontinuities and
reduces nonlinearity at the poles of the azimuth-elevation coordinate system.
Our computer simulations show that the proposed VMF measurement noise model
based filters outperform the normal distribution based algorithms in accuracy
in a scenario where close-to-pole measurements occur frequently.Comment: 5 page
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