3,592 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
Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks
In distributed target tracking for wireless sensor networks, agreement on the
target state can be achieved by the construction and maintenance of a
communication path, in order to exchange information regarding local likelihood
functions. Such an approach lacks robustness to failures and is not easily
applicable to ad-hoc networks. To address this, several methods have been
proposed that allow agreement on the global likelihood through fully
distributed belief consensus (BC) algorithms, operating on local likelihoods in
distributed particle filtering (DPF). However, a unified comparison of the
convergence speed and communication cost has not been performed. In this paper,
we provide such a comparison and propose a novel BC algorithm based on belief
propagation (BP). According to our study, DPF based on metropolis belief
consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus
is the fastest in tree graphs. Moreover, we found that BC-based DPF methods
have lower communication overhead than data flooding when the network is
sufficiently sparse
Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing
In this work the dynamic compressive sensing (CS) problem of recovering
sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear
measurements is explored from a Bayesian perspective. While there has been a
handful of previously proposed Bayesian dynamic CS algorithms in the
literature, the ability to perform inference on high-dimensional problems in a
computationally efficient manner remains elusive. In response, we propose a
probabilistic dynamic CS signal model that captures both amplitude and support
correlation structure, and describe an approximate message passing algorithm
that performs soft signal estimation and support detection with a computational
complexity that is linear in all problem dimensions. The algorithm, DCS-AMP,
can perform either causal filtering or non-causal smoothing, and is capable of
learning model parameters adaptively from the data through an
expectation-maximization learning procedure. We provide numerical evidence that
DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety
of operating conditions. We further describe the result of applying DCS-AMP to
two real dynamic CS datasets, as well as a frequency estimation task, to
bolster our claim that DCS-AMP is capable of offering state-of-the-art
performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure
Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging
The implementation challenges of cooperative localization by dual
foot-mounted inertial sensors and inter-agent ranging are discussed and work on
the subject is reviewed. System architecture and sensor fusion are identified
as key challenges. A partially decentralized system architecture based on
step-wise inertial navigation and step-wise dead reckoning is presented. This
architecture is argued to reduce the computational cost and required
communication bandwidth by around two orders of magnitude while only giving
negligible information loss in comparison with a naive centralized
implementation. This makes a joint global state estimation feasible for up to a
platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion
for the considered setup, based on state space transformation and
marginalization, is presented. The transformation and marginalization are used
to give the necessary flexibility for presented sampling based updates for the
inter-agent ranging and ranging free fusion of the two feet of an individual
agent. Finally, characteristics of the suggested implementation are
demonstrated with simulations and a real-time system implementation.Comment: 14 page
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