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The probabilistic multi-hypothesis tracking (PMHT) algorithm has been successfully applied to a simulated multi-static active sonar data set that contains a single constant velocity target in varying amounts of clutter [1]. The simulated data set in that study contained negligible registration error was therefore easily registered to a common frame of reference for use in a centralized tracking architecture...
The estimation of a vehiclepsilas dynamic state is one of the most fundamental data fusion tasks for intelligent traffic applications. For that, motion models are applied in order to increase the accuracy and robustness of the estimation. This paper surveys numerous (especially curvilinear) models and compares their performance using a tracking tasks which includes the fusion of GPS and odometry data...
Fusion of a simulation model and observation data has been investigated extensively for the purpose of data assimilation in geophysics. The inaccuracy of the parameters, initial conditions, or boundary conditions causes a discrepancy in the simulation results and the actual phenomenon. The present paper describes the parameter identification of a pressure regulator with a nonlinear structure by sequential...
An improved neurobiologically inspired algorithm for situation awareness in the maritime domain takes real-time tracking information and learns motion pattern models based on temporal associations between vessel events enabling conditional probabilities between events to be learned incrementally and locally. These learned weights are used for future vessel location prediction. Improvements in prediction...
In the scope of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, we investigate the potential of pre-existing road traffic sensors for pedestrian crossing detection. Two road traffic video-sensors provide spatial occupancy rates on areas along a crosswalk. We propose to correct pedestrian under-detection with a double fusion process composed of inter-sensor...
In this paper a new logical arbitration protocol for fusion of inconsistent information is designed. It defines a selection of models of a premise set in a multi-modal logic that uses the standard format of adaptive logics. The selected models are obtained by a counting procedure on the derivable data conflicting among the various sources. Peculiar of this approach is the definition of weights for...
Time series of optical satellite images acquired at high spatial resolution constitute an important source of information for crop monitoring, in particular for keeping track of crop harvest. However, the quantity of information extracted from this source is often restricted by acquisition gaps and uncertainty of radiometric values. This paper presents a novel approach that addresses this issue by...
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...
In this paper, we establish a link between belief functions on real numbers and the maximal coherent sets obtained in the framework of possibilistic distributions. Having proposed an original disjunctive rule of combination in the framework of continuous belief functions, we demonstrate theoretically that maximal coherent sets can be viewed as a particular case in the framework of belief functions.
Enabling situation awareness necessitates working with processes capable of identifying domain specific activities. This paper addresses metrics developed to assess research level systems and to measure their performance in providing those processes. The metrics fall into four dimensions; confidence, purity, cost utility, and timeliness. The bulk of the discussion will provide an overview of each...
Surveillance of large land, air or sea areas with a multitude of sensor and sensor types typically generates huge amounts of data. Human operators trying to establish individual or collective maritime situation awareness are often overloaded by this information. In order to help them cope with this information overload, we have developed a combined methodology of data visualization, interaction and...
Situation Awareness involves both the ability to identify and recognize the given activities and in assessing their importance through situation assessment. In this paper we look at a number of metrics that can be used in an information fusion framework to evaluate how well our assessment tools work at discriminating information from data. We begin our discussion by first providing a set of definitions...
In this paper we propose an approach for detecting anomalies in data from visual surveillance sensors. The approach includes creating a structure for representing data, building ldquonormal modelsrdquo by filling the structure with data for the situation at hand, and finally detecting deviations in the data. The approach allows detections based on the incorporation of a priori knowledge about the...
This paper presents a method for the management of information dissemination in a vehicular network (VANET). Due to the particularities of the application (ad hoc network, dynamical nodes, broadcast messages), an algorithm has been developed to fuse and combine data in distributed systems. Matching spatial information is made easier by the use of a numerical map as support of a database. A model of...
In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering...
Todaypsilas asymmetric threats put new challenges on military decision making. As new technology develops we have new possibilities to support decision making in such environments. However, it is important that the tools developed take into account userspsila (commanderspsila) decision needs. This paper presents some initial user studies of Swedish commanders testing a prototype application developed...
Enemy Courses of Action (ECOAs) play a central role in the process of situation development in military decision-making. In order to reason about ECOAs, it would be necessary to adequately represent them in a formalism that allows for automatic reasoning. In this paper, we examine the benefits and drawbacks of representing ECOAs within several frameworks that have been encoded as OWL ontologies.
The use of a suitably expressive underlying situation model allows the detection of differences in the situation awareness of an operator as compared to a situation model estimated from low-level tracks. A grammatically based situation model allows efficient construction of these models. Operator speech and actions can be interpreted as statements about the situation and expressed using the same model...
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