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This paper presents a method for identifying dynamic models of autonomous underwater vehicles (AUV) from logged data and a physically motivated model structure. Such models are instrumental for model-based control system design, but also for integrated navigation systems. We motive our work from the perspective of developing second generation integrated navigation systems, which use a sensor fusion...
Assume an ESA (electronically steered antenna) radar. In between the surveillance mode operations (where the radar completes one scan of the surveillance area), this radar has spare time for (at least one) additional mode which could be optimized for various special requirements. The optimization is often translated to the choice of the next track to update. In this paper we propose a resource allocation...
This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles...
The main challenge of future phased array multifunction radar will be to optimise the radar time budget to avoid deleterious overload effects. For this purpose, we propose a new technique for priority assignment of Search Domains. Then, we have developed new algorithms for most threatening trajectories computation based on Calculus of Variations approach developed for "shortest path computation"...
This paper addresses the problem of designing objective functions for autonomous surveillance-target search & tracking (S&T) - by unmanned aerial vehicles (UAVs). A typical S&T mission inherently includes multiple, most often conflicting, objectives such as detection, survival, and tracking. A common approach to cope with this issue is to optimize a convex combination (weighted sum) of...
This contribution aims at unifying two trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (slam) applications, utilizing the FastSLAM algorithm. The second one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. An algorithm is introduced,...
Information fusion engine for real-time decision- making (INFERD) is a perceptual information fusion engine designed and developed for the purpose of cyber attack tracking and network situational awareness. While the original application was cyber orientated, the engine itself is designed to generalize and has been ported to other application environments such as maritime domain awareness and medical...
We present a new approach for computing with fuzzy sets based on interval analysis techniques. Our proposed method is capable of managing multi-dimensional continuous membership functions of arbitrary form, such as, piecewise affine functions or non-linear expressions without any restrictions regarding convexity. We present a formal representation of fuzzy sets that allows to easily cast fuzzy problems...
In this paper, we present a new general method for image fusion based on partial differential equation (PDE). We propose to combine pixel-level fusion and diffusion processes through one single powerful equation. The insertion of the relevant information contained in sources is achieved in the fused image by reversing the diffusion process. To solve the well-known instability problem of an inverse...
This paper presents a contour-based multisensor image registration algorithm. The characteristic of this approach is that the registration parameters are calculated according to the centroids and the long axes of matched contour pairs in the images to be registered It overcomes the difficulties of control point detection and correspondence in feature- based registration techniques. The geometrical...
This contribution presents a unified framework for localization and tracking in sensor networks based on fusing a variety of signal energy measurements as provided by for instance acoustic, seismic, magnetic, radio, microwave and infrared sensors. The received energy from such sensors generally decays exponentially, and a log range model is introduced for the sensor observations in logarithmic scale,...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three issues: (i) we show the equivalence between the...
A model for vehicle motion on a road network Is developed using an enumeration of feasible routes. Combined with a generic stochastic model of distance travelled, a predicted pdf of vehicle position is derived as a mixture. This approach allows prior information on vehicle intent and behaviour to be included via the mixture weights. Illustrative examples are given using a second-order linear-Gaussian...
The focus of most information fusion research, so far, has been on the technology, i.e. information processing techniques and algorithms. Consequently, there is a lack of research concerning the actual usage of information fusion systems in terms of cognitive and organisational issues such as supporting both individual and group decision making. This paper provides a retrospective of information fusion...
This paper presents a Bayesian off-line fusion segmentation method, applied to the code tracking in a multi-carrier GPS receiver. The tracking is realized with discriminator values obtained on the different carrier frequencies. We suppose that the evolution of the pseudo-ranges satellites-receiver is piecewise linear. We propose a Bayesian method for the fusion of change detection models in the discriminators...
This paper studies the notion of information evaluation in fusion. It starts from some recommendations defined by NATO relatively to information evaluation in intelligence. It shows that these recommendations, even if imprecise and ambiguous, point out three notions which should be taken into account in order to define information evaluation. It proposes a model of information evaluation, which takes...
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...
An algorithm for detection and tracking of multiple targets using bearings measurements from several sensors is developed. The algorithm is an implementation of a multiple hypothesis tracker with pruning of unlikely hypotheses. Tracking conditional on each hypothesis can be performed using any suitable filtering approximation. In this paper a range- parameterized unscented Kalman filter is used. Each...
Most of studies in the field of information fusion focus on the production of high-level information from low-level data. The challenge is then to fuse this high-level information to produce a global and coherent information. Another approach consists in interpreting data as high-level information and fuse it at once. Our approach relies on the use of conceptual graphs model. The model is widely used...
The idea of particle filter is to represent probability density function (PDF) of nonlinear/non-Gaussian system by a set of random samples. One of the key issue of particle filter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particle filter. The proposal distributions integrate the current observation, thus...
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