136 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
Distributed Estimation with Information-Seeking Control in Agent Network
We introduce a distributed, cooperative framework and method for Bayesian
estimation and control in decentralized agent networks. Our framework combines
joint estimation of time-varying global and local states with
information-seeking control optimizing the behavior of the agents. It is suited
to nonlinear and non-Gaussian problems and, in particular, to location-aware
networks. For cooperative estimation, a combination of belief propagation
message passing and consensus is used. For cooperative control, the negative
posterior joint entropy of all states is maximized via a gradient ascent. The
estimation layer provides the control layer with probabilistic information in
the form of sample representations of probability distributions. Simulation
results demonstrate intelligent behavior of the agents and excellent estimation
performance for a simultaneous self-localization and target tracking problem.
In a cooperative localization scenario with only one anchor, mobile agents can
localize themselves after a short time with an accuracy that is higher than the
accuracy of the performed distance measurements.Comment: 17 pages, 10 figure
Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen- dence graph (CIG)
of a high-dimensional discrete-time Gaus- sian vector random process from
finite-length observations. Our approach does not rely on a parametric model
(such as, e.g., an autoregressive model) for the vector random process; rather,
it only assumes certain spectral smoothness proper- ties. The proposed
inference scheme is compressive in that it works for sample sizes that are
(much) smaller than the number of scalar process components. We provide
analytical conditions for our method to correctly identify the CIG with high
probability.Comment: to appear in Proc. IEEE ICASSP 201
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure
Generic Correlation Increases Noncoherent MIMO Capacity
We study the high-SNR capacity of MIMO Rayleigh block-fading channels in the
noncoherent setting where neither transmitter nor receiver has a priori channel
state information. We show that when the number of receive antennas is
sufficiently large and the temporal correlation within each block is "generic"
(in the sense used in the interference-alignment literature), the capacity
pre-log is given by T(1-1/N) for T<N, where T denotes the number of transmit
antennas and N denotes the block length. A comparison with the widely used
constant block-fading channel (where the fading is constant within each block)
shows that for a large block length, generic correlation increases the capacity
pre-log by a factor of about four.Comment: To be presented at IEEE Int. Symp. Inf. Theory (ISIT) 2013, Istanbul,
Turke
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