5,726 research outputs found
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
Nonlinear state space smoothing using the conditional particle filter
To estimate the smoothing distribution in a nonlinear state space model, we
apply the conditional particle filter with ancestor sampling. This gives an
iterative algorithm in a Markov chain Monte Carlo fashion, with asymptotic
convergence results. The computational complexity is analyzed, and our proposed
algorithm is successfully applied to the challenging problem of sensor fusion
between ultra-wideband and accelerometer/gyroscope measurements for indoor
positioning. It appears to be a competitive alternative to existing nonlinear
smoothing algorithms, in particular the forward filtering-backward simulation
smoother.Comment: Accepted for the 17th IFAC Symposium on System Identification
(SYSID), Beijing, China, October 201
Minimum information loss fusion in distributed sensor networks
A key assumption of distributed data fusion is
that individual nodes have no knowledge of the global network
topology and use only information which is available locally.
This paper considers the weighted exponential product (WEP)
rule as a methodology for conservatively fusing estimates with
an unknown degree of correlation between them. We provide a
preliminary investigation into how the methodology for selecting
the mixing parameter can be used to minimize the information
loss in the fused covariance as opposed to reducing the Shannon
entropy, and hence maximize the information of the fused
covariance. Our results suggest that selecting a mixing parameter
which minimizes the information loss ensures that information
which is exclusive to the estimates from one source is not lost
during the fusion process. These results indicate that minimizing
the information loss provides a robust technique for selecting the
mixing parameter in WEP fusion
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
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