42,206 research outputs found
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Reduced Complexity Filtering with Stochastic Dominance Bounds: A Convex Optimization Approach
This paper uses stochastic dominance principles to construct upper and lower
sample path bounds for Hidden Markov Model (HMM) filters. Given a HMM, by using
convex optimization methods for nuclear norm minimization with copositive
constraints, we construct low rank stochastic marices so that the optimal
filters using these matrices provably lower and upper bound (with respect to a
partially ordered set) the true filtered distribution at each time instant.
Since these matrices are low rank (say R), the computational cost of evaluating
the filtering bounds is O(XR) instead of O(X2). A Monte-Carlo importance
sampling filter is presented that exploits these upper and lower bounds to
estimate the optimal posterior. Finally, using the Dobrushin coefficient,
explicit bounds are given on the variational norm between the true posterior
and the upper and lower bounds
Initial Conditions for Large Cosmological Simulations
This technical paper describes a software package that was designed to
produce initial conditions for large cosmological simulations in the context of
the Horizon collaboration. These tools generalize E. Bertschinger's Grafic1
software to distributed parallel architectures and offer a flexible alternative
to the Grafic2 software for ``zoom'' initial conditions, at the price of large
cumulated cpu and memory usage. The codes have been validated up to resolutions
of 4096^3 and were used to generate the initial conditions of large
hydrodynamical and dark matter simulations. They also provide means to generate
constrained realisations for the purpose of generating initial conditions
compatible with, e.g. the local group, or the SDSS catalog.Comment: 12 pages, 11 figures, submitted to ApJ
Ensemble updating of binary state vectors by maximising the expected number of unchanged components
In recent years, several ensemble-based filtering methods have been proposed
and studied. The main challenge in such procedures is the updating of a prior
ensemble to a posterior ensemble at every step of the filtering recursions. In
the famous ensemble Kalman filter, the assumption of a linear-Gaussian state
space model is introduced in order to overcome this issue, and the prior
ensemble is updated with a linear shift closely related to the traditional
Kalman filter equations. In the current article, we consider how the ideas
underlying the ensemble Kalman filter can be applied when the components of the
state vectors are binary variables. While the ensemble Kalman filter relies on
Gaussian approximations of the forecast and filtering distributions, we instead
use first order Markov chains. To update the prior ensemble, we simulate
samples from a distribution constructed such that the expected number of equal
components in a prior and posterior state vector is maximised. We demonstrate
the performance of our approach in a simulation example inspired by the
movement of oil and water in a petroleum reservoir, where also a more na\"{i}ve
updating approach is applied for comparison. Here, we observe that the
Frobenius norm of the difference between the estimated and the true marginal
filtering probabilities is reduced to the half with our method compared to the
na\"{i}ve approach, indicating that our method is superior. Finally, we discuss
how our methodology can be generalised from the binary setting to more
complicated situations
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