1,871 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
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
Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks
Localization in wireless sensor networks not only provides a node with its
geographical location but also a basic requirement for other applications such
as geographical routing. Although a rich literature is available for
localization in static WSN, not enough work is done for mobile WSNs, owing to
the complexity due to node mobility. Most of the existing techniques for
localization in mobile WSNs uses Monte-Carlo localization, which is not only
time-consuming but also memory intensive. They, consider either the unknown
nodes or anchor nodes to be static. In this paper, we propose a technique
called Dead Reckoning Localization for mobile WSNs. In the proposed technique
all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in
DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are
localized for the first time using three anchor nodes. For their subsequent
localizations, only two anchor nodes are used. The proposed technique estimates
two possible locations of a node Using Bezouts theorem. A dead reckoning
approach is used to select one of the two estimated locations. We have
evaluated DRLMSN through simulation using Castalia simulator, and is compared
with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201
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
Group Importance Sampling for Particle Filtering and MCMC
Bayesian methods and their implementations by means of sophisticated Monte
Carlo techniques have become very popular in signal processing over the last
years. Importance Sampling (IS) is a well-known Monte Carlo technique that
approximates integrals involving a posterior distribution by means of weighted
samples. In this work, we study the assignation of a single weighted sample
which compresses the information contained in a population of weighted samples.
Part of the theory that we present as Group Importance Sampling (GIS) has been
employed implicitly in different works in the literature. The provided analysis
yields several theoretical and practical consequences. For instance, we discuss
the application of GIS into the Sequential Importance Resampling framework and
show that Independent Multiple Try Metropolis schemes can be interpreted as a
standard Metropolis-Hastings algorithm, following the GIS approach. We also
introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS.
The first one, named Group Metropolis Sampling method, produces a Markov chain
of sets of weighted samples. All these sets are then employed for obtaining a
unique global estimator. The second one is the Distributed Particle
Metropolis-Hastings technique, where different parallel particle filters are
jointly used to drive an MCMC algorithm. Different resampled trajectories are
compared and then tested with a proper acceptance probability. The novel
schemes are tested in different numerical experiments such as learning the
hyperparameters of Gaussian Processes, two localization problems in a wireless
sensor network (with synthetic and real data) and the tracking of vegetation
parameters given satellite observations, where they are compared with several
benchmark Monte Carlo techniques. Three illustrative Matlab demos are also
provided.Comment: To appear in Digital Signal Processing. Related Matlab demos are
provided at https://github.com/lukafree/GIS.gi
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