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The problem is joint detection and tracking of possibly several objects moving through a region of interest. A wireless sensor network (WSN), deployed in the region, collects the acoustic energy measurements and sends them to the fusion center for processing. The problem is cast in the sequential Bayesian estimation framework and solved using a particle filter. The number of objects is unknown and...
In this paper, we present two novel methods to handle the fusion of multiple Bayesian Network knowledge fragments which we termed N-Combinator and N-Clone. In DSO National Laboratories, we have developed a cognition based dynamic reasoning machine called D'Brain capable of performing high level data fusion. Knowledge is encapsulated in D'Brain as Bayesian Networks knowledge fragments. D'Brain is dynamic...
This panel position paper discusses advantages and challenges of multi agent fusion systems (MAS) with respect to the modeling flexibility and fusion reliability. We argue that the MAS paradigm in combination with rigorous modeling and inference methods can facilitate design of theoretically and technically sound fusion systems. This is illustrated with the help of a MAS approach to Bayesian fusion...
The paper presents an algorithm for detection and a subsequent information gain driven search for an unaccounted point source of relatively low-level gamma radiation. Source detection and parameter estimation are carried out jointly in the Bayesian framework using a particle filter. The observer control vector consists of the next sensor location and the exposure time. During the pre-detection search,...
Many problems involve joint decision and estimation, where qualities of decision and estimation affect each other. This paper proposes an integrated approach based on a new Bayes risk, which is a generalization of those for decision and estimation separately. Theoretical results of the optimal joint decision and estimation that minimizes the new Bayes risk are presented. The power of the new approach...
In a net-centric world, systems will be required to fuse data from geographically dispersed, heterogeneous information sources operating asynchronously, to produce up-to-date, mission-relevant knowledge to inform commanders. Realizing this vision requires overcoming a number of technical challenges. Among these is the need for semantic interoperability among systems with different internal data models...
Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training...
Given an area where an unknown number of unaccounted radioactive sources potentially exist, and using gamma- radiation count measurements collected at known locations within this area, the problem is to estimate the number of sources as well as their locations and intensities. Two approaches are investigated. The first is based on the maximum likelihood estimation and the generalised maximum likelihood...
The construction of belief networks is a widely used methodology for high level fusion modeling. While some of the components of a belief network deal with ambiguous (probabilistic) data, others may deal with vague (possibilistic) data. Given the need to represent both probabilistic and possibilistic components in a single belief network, a framework and toolset for building Hybrid networks, utilizing...
In multi-sensor multi-target bearings-only tracking we often see false intersections of bearings known as ghosts. When the bearing measurements from each sensor have been associated to form sequences termed threads, the problem is to associate pairs of threads to identify the true target intersections. In this paper we present two algorithms: (i) classical bayesian thread association (CBTA) and (ii)...
In this paper a method is introduced based on the concept of Bayesian networks (BNs), which is applied to model sensor fusion. Sensors can be characterised as real dynamical systems with specific physical functional principles, allowing to determine the value of a physical state of interest within certain ranges of tolerance. The measurements of the sensors are affected by external, e.g. environmental...
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...
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...
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...
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...
In nonlinear Bayesian estimation it is generally inevitable to incorporate approximate descriptions of the exact estimation algorithm. There are two possible ways to involve approximations: Approximating the nonlinear stochastic system model or approximating the prior probability density function. The key idea of the introduced novel estimator called Hybrid Density Filter relies on approximating the...
As part of the track-management process it is necessary to know when new tracks start and when old ones die. Thus some knowledge of the theory of detection of statistical changes is important, and the purpose of this talk is to give the audience some overview of what is available. Specifically, we shall discuss sequential testing, this information necessary as a precursor to an understanding of the...
Many problems involve both decision and estimation where the performance of decision and estimation affects each other. They are usually solved by a two-stage strategy: decision-then-estimation or estimation-then-decision, which suffers from several serious drawbacks. A more integrated solution is preferred. Such an approach was proposed in X.R. Li (July 2007). It is based on a new Bayes risk as a...
This paper examines the ordering of measurement updates for a general Bayesian inference problem and its impact on the estimation of the posterior distribution. The approach used compares the expected improvement to the posterior from various types of potential measurements, taking into account the current estimated prior but not the actual measurements, to determine the optimal measurement to perform...
Data fusion within the evidential reasoning framework is a well established, robust and conservative technique to fuse uncertain information from multiple sensors. A number of fusion methods within this formalism were introduced including Dempster-Shafer theory (DST) fusion, Dezert Samarandche fusion (DSmT), and Smets' transferable belief model (TBM) based fusion. However, the impact of fusion on...
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