28,484 research outputs found
Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications
This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.Comment: In Proc. of the 2012 American Control Conferenc
Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems
In this paper, a novel feedback control-based particle filter algorithm for
the continuous-time stochastic hybrid system estimation problem is presented.
This particle filter is referred to as the interacting multiple model-feedback
particle filter (IMM-FPF), and is based on the recently developed feedback
particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for
each discrete mode, and an exact filter recursion for the mode association
probability. The proposed IMM-FPF represents a generalization of the
Kalmanfilter based IMM algorithm to the general nonlinear filtering problem.
The remarkable conclusion of this paper is that the IMM-FPF algorithm retains
the innovation error-based feedback structure even for the nonlinear problem.
The interaction/merging process is also handled via a control-based approach.
The theoretical results are illustrated with the aid of a numerical example
problem for a maneuvering target tracking application
Modeling Longitudinal Oscillations of Bunched Beams in Synchrotrons
Longitudinal oscillations of bunched beams in synchrotrons have been analyzed
by accelerator physicists for decades, and a closed theory is well-known [1].
The first modes of oscillation are the coherent dipole mode, quadrupole mode,
and sextupole mode. Of course, these modes of oscillation are included in the
general theory, but for developing RF control systems, it is useful to work
with simplified models. Therefore, several specific models are analyzed in the
paper at hand. They are useful for the design of closed-loop control systems in
order to reach an optimum performance with respect to damping the different
modes of oscillation. This is shown by the comparison of measurement and
simulation results for a specific closed-loop control system.Comment: 14 pages, 14 figure
The Neural Particle Filter
The robust estimation of dynamically changing features, such as the position
of prey, is one of the hallmarks of perception. On an abstract, algorithmic
level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing
signals based on the history of observations, provides a mathematical framework
for dynamic perception in real time. Since the general, nonlinear filtering
problem is analytically intractable, particle filters are considered among the
most powerful approaches to approximating the solution numerically. Yet, these
algorithms prevalently rely on importance weights, and thus it remains an
unresolved question how the brain could implement such an inference strategy
with a neuronal population. Here, we propose the Neural Particle Filter (NPF),
a weight-less particle filter that can be interpreted as the neuronal dynamics
of a recurrently connected neural network that receives feed-forward input from
sensory neurons and represents the posterior probability distribution in terms
of samples. Specifically, this algorithm bridges the gap between the
computational task of online state estimation and an implementation that allows
networks of neurons in the brain to perform nonlinear Bayesian filtering. The
model captures not only the properties of temporal and multisensory integration
according to Bayesian statistics, but also allows online learning with a
maximum likelihood approach. With an example from multisensory integration, we
demonstrate that the numerical performance of the model is adequate to account
for both filtering and identification problems. Due to the weightless approach,
our algorithm alleviates the 'curse of dimensionality' and thus outperforms
conventional, weighted particle filters in higher dimensions for a limited
number of particles
Tight Upper and Lower Bounds to the Information Rate of the Phase Noise Channel
Numerical upper and lower bounds to the information rate transferred through
the additive white Gaussian noise channel affected by discrete-time
multiplicative autoregressive moving-average (ARMA) phase noise are proposed in
the paper. The state space of the ARMA model being multidimensional, the
problem cannot be approached by the conventional trellis-based methods that
assume a first-order model for phase noise and quantization of the phase space,
because the number of state of the trellis would be enormous. The proposed
lower and upper bounds are based on particle filtering and Kalman filtering.
Simulation results show that the upper and lower bounds are so close to each
other that we can claim of having numerically computed the actual information
rate of the multiplicative ARMA phase noise channel, at least in the cases
studied in the paper. Moreover, the lower bound, which is virtually
capacity-achieving, is obtained by demodulation of the incoming signal based on
a Kalman filter aided by past data. Thus we can claim of having found the
virtually optimal demodulator for the multiplicative phase noise channel, at
least for the cases considered in the paper.Comment: 5 pages, 2 figures. Accepted for presentation at ISIT 201
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