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Subject matter expert assessments can include both assignment and linguistic uncertainty. This paper examines assessments containing linguistic uncertainty associated with a qualitative description of a specific state of interest and the assignment uncertainty associated with assigning the state to a particular qualitative value. A Bayesian approach is examined to simultaneously quantify both assignment...
The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Due to limited sensor resolution capabilities, group targets (i.e., a number of closely spaced targets moving in a coordinated fashion) may show a similar detection pattern as extended objects, namely a varying number of detections whose spread is determined...
Anomaly Detection (AD) in remotely sensed airborne hyperspectral images has been proven valuable in many applications. Within the AD approach that defines the spectral anomalies with respect to a statistical model for the background, reliable background PDF estimation is essential to a successful outcome. This paper proposes a new Bayesian strategy for learning a non-Gaussian mixture model for the...
The theory of belief functions has been formalized in continuous domain for pattern recognition. Some applications use assumption of Gaussian models. However, this assumption is reductive. Indeed, some data are not symmetric and present property of heavy tails. It is possible to solve these problems by using a class of distributions called α-stable distributions. Consequently, we present in this paper...
In many pervasive scenarios (e.g., emergency management or health care), operators need to exchange data and information and collaborate in order to carry on a collaborative job, but communication features can be lacking on the spot. Therefore, mobile ad-hoc networks are valuable solutions to let them coordinate. While executing some activities, nodes can move in the area and, hence, disconnect from...
Bayesian filters provide a statistical tool for dealing with measurement uncertainty. Bayesian filters estimate a state of dynamic system from noisy observations. These filters represent the state by random variable and in each time step probability distribution over random variable represents the uncertainty. If estimate is needed with every new measurement, it is suitable to use recursive filter...
Using maximum likelihood estimation (MLE) to estimate the parameters in a Weibull distribution will lead to a biased estimation of the shape parameter when the sample size is small or too few failures are observed. This bias may lead to inaccurate reliability point estimates. In addition, with few data points available in the calculation, the uncertainty of the estimated parameters is high, which...
This paper proposed a two-step indoor location estimation algorithm. A Bayesian approach is adopted to incorporate prior PDF of the concerned variables to obviate the use of training sequences. Then the preliminary estimated parameters were combined with the path loss model. The estimated location was modified in its neighborhood to achieve greater accuracy. Considering the great difficulty in acquiring...
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