2,952 research outputs found
Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.Comment: 5 pages, 1 figur
Scaled unscented transform Gaussian sum filter: theory and application
In this work we consider the state estimation problem in
nonlinear/non-Gaussian systems. We introduce a framework, called the scaled
unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas:
the scaled unscented Kalman filter (SUKF) based on the concept of scaled
unscented transform (SUT), and the Gaussian mixture model (GMM). The SUT is
used to approximate the mean and covariance of a Gaussian random variable which
is transformed by a nonlinear function, while the GMM is adopted to approximate
the probability density function (pdf) of a random variable through a set of
Gaussian distributions. With these two tools, a framework can be set up to
assimilate nonlinear systems in a recursive way. Within this framework, one can
treat a nonlinear stochastic system as a mixture model of a set of sub-systems,
each of which takes the form of a nonlinear system driven by a known Gaussian
random process. Then, for each sub-system, one applies the SUKF to estimate the
mean and covariance of the underlying Gaussian random variable transformed by
the nonlinear governing equations of the sub-system. Incorporating the
estimations of the sub-systems into the GMM gives an explicit (approximate)
form of the pdf, which can be regarded as a "complete" solution to the state
estimation problem, as all of the statistical information of interest can be
obtained from the explicit form of the pdf ...
This work is on the construction of the Gaussian sum filter based on the
scaled unscented transform
Statistical Inference in the Duffing System with the Unscented Kalman Filter
We investigate the accuracy of inference in a chaotic dynamical sys- tem (Duffing oscillator) with the Unscented Kalman Filter, and quantify the dependence on the sample size, the signal to noise ratio and the initialization
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
- …