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This paper presents comparative results of a model for multiple camera fusion, which is based on Dezert-Smarandache theory of evidence. Our architecture works at the decision level to track objects on a ground plane using predefined zones, producing useful information for surveillance tasks such as behavior recognition. Decisions from cameras are generated by applying a perspective-based basic belief...
In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have...
The challenge of modern sensor systems is besides the tracking of targets more and more their classification. The knowledge of the target class has significant influence on the identification, threat evaluation and weapon assignment process of large systems. Especially, considering new types of threats in anti asymmetric warfare the knowledge of a target class has an important drawback. Also the target...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from the missed detection. In the case of zero...
This contribution presents a fusion method for multivariate stereo and spectral series with the purpose of obtaining 3D information. The image series are gained using a camera array with spectral filters. In order to register them, features that are invariant with respect to the intensity values in the images are extracted. The fusion approach is region based and uses characteristics like their size,...
We propose an asymptotically optimum test for the problem of decentralized sequential hypothesis testing in continuous time, in the case where the sensors have full local memory and no feedback from the fusion center. According to our scheme, the sensors perform locally repeated SPRTs and communicate, asynchronously, their one-bit decisions to the fusion center. The fusion center in turn uses the...
This paper considers the problem of the classification of objects observed by vehicle embedded sensors. We propose a general architecture and an algorithm to perform multisensor fusion for the classification purpose. The proposed solution has to be robust and flexible. The robustness is essential because this system is for safety applications. The flexibility is ensured by a modular architecture alongside...
Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision...
Identification of tracked objects is a key capability of automated surveillance and information systems for air, surface and subsurface (maritime), and ground environments, improving situational awareness and offering decision support to operational users. The Bayesian-based identification data combining process (IDCP) provides an effective instrument for fusion of uncertain identity indications from...
Different information theoretic sensor management approaches are compared in a Bayesian target-tracking problem. Specifically, the performance using the expected Renyi divergence with different parameter values is compared theoretically and experimentally. Included is the special case in which the expected Renyi divergence is equal to the expected Kullback-Leibler divergence, which is also equivalent...
Statistical mechanics has proven to be a useful model for drawing inferences about the collective behavior of individual objects that interact according to a known force law (which for a more general usage is referred to as interacting units.). Collective behavior is determined not by computing F = ma for each interacting unit because the problem is mathematically intractable. Instead, one computes...
In theory, a good joint particle filter allows to approximate the exact Bayesian filter solution arbitrarily well. This has motivated a strong and successful development of single target tracking particle filters. Nevertheless, for tracking multiple closely spaced maneuvering targets, there is evidence in literature which seems to contradict the theoretical expectation. The mystery of this apparent...
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature...
This paper presents a theoretical framework for Bayesian estimation in the case of imprecisely known probability density functions. The lack of knowledge about the true density functions is represented by sets of densities. A formal Bayesian estimator for these sets is introduced, which is intractable for infinite sets. To obtain a tractable filter, properties of convex sets in form of convex polytopes...
This paper introduces an information theoretic approach to verification of causal models in modular Bayesian fusion systems. We assume distributed fusion systems which are gradually extended by adding new modules, each having a limited domain knowledge captured in local Bayesian networks. However, since different modules originate from different, independent design processes important dependencies...
Recent attention in quickest change detection in a multi-sensor scenario has been on the case where the densities of the observations at all the sensors change instantaneously at the time of disruption. In this work, we consider a scenario where change propagates across the sensors and its propagation can be modeled as a Markov process. A centralized, Bayesian version of this problem, with a common...
To cope with asymmetric threats in an increasingly network centric environment, todaypsilas command support systems must interoperate with a diverse collection of other systems. As a natural consequence, the focus is changing from data fusion to knowledge fusion. This new reality creates the need for advanced techniques that exploit not only the syntactic structure of knowledge bases, but also the...
The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Extended objects may give rise to more than one detection per opportunity where the scattering centers may vary from scan to scan. On the other end, group targets (i. e., a number of closely spaced targets moving in a coordinated fashion) often will not...
Collectively moving object clusters are of particular interest in certain applications and have to be tracked as separate aggregated entities consisting of an unknown number of individuals. Tracking of convoys or larger vehicles in wide area ground surveillance are important examples. The objects of interest are usually considered as point source objects, i. e., compared to the sensor resolution their...
We present local Bayesian fusion approaches for the reduction of storage and computational costs of Bayesian fusion which is detached from fixed modelling assumptions. Using local approaches, Bayesian fusion is not performed in detail on the whole space that is spanned by the quantities of interest but only locally - at least in regions that are task relevant with a high probability. These regions...
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