14,336 research outputs found
Application of probabilistic PCR5 Fusion Rule for Multisensor Target Tracking
This paper defines and implements a non-Bayesian fusion rule for combining
densities of probabilities estimated by local (non-linear) filters for tracking
a moving target by passive sensors. This rule is the restriction to a strict
probabilistic paradigm of the recent and efficient Proportional Conflict
Redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing
basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is
defined. It is shown that p-PCR5 is more robust to an erroneous modeling and
allows to keep the modes of local densities and preserve as much as possible
the whole information inherent to each densities to combine. In particular,
p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the
hypotheses are too distant in regards to their deviations. This new p-PCR5 rule
has been tested on a simple example of distributed non-linear filtering
application to show the interest of such approach for future developments. The
non-linear distributed filter is implemented through a basic particles
filtering technique. The results obtained in our simulations show the ability
of this p-PCR5-based filter to track the target even when the models are not
well consistent in regards to the initialization and real cinematic
Improving acoustic vehicle classification by information fusion
We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac
Combining link and content-based information in a Bayesian inference model for entity search
An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie
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