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Evaluation of hybrid fusion 2+approach for providing air-to-air situational awareness and threat assessment

Evaluation of hybrid fusion 2+approach for providing air-to-...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XV

作者： Lee, Kangjin David Wiesenfeld, Eric Colony, Mike Decis Analyt Corp 1235 S Clark St Suite 400 Arlington VA 22202 USA

ISBN: (纸本)0819462918

Modem combat aircraft pilots increasingly rely on high-level fusion models (JDL Levels 2/3) to provide real-time engagement support in hostile situations. These models provide both Situational Awareness (SA) and Threat Assessment (TA) based on data and the relationships between the data. This information represents two distinct classes of uncertainty: vagueness and ambiguity. To address the needs associated with modeling both of these types of data uncertainty, an innovative hybrid approach was recently introduced, combining probability theory and possibility theory into a unified computational framework. The goal of this research is to qualitatively and quantitatively address the advantages and disadvantages of adopting this hybrid framework as well as identifying instances in which the combined model outperforms or is more appropriate than more classical inference approaches. To accomplish this task, domain specific models will be developed using different theoretical approaches and conventions, and then evaluated in comparison to situational ground truth to determine their accuracy and fidelity. Additionally, the performance tradeoff between accuracy and complexity will be examined in terms of computational cost to determine both the advantages and disadvantages of each approach.

关键词： fusion 2+ Situation Awareness Bayesian networks fuzzy logic junction tree algorithm min/max algorithm

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Statistical comparison of a hybrid approach with approximate and exact inference models for fusion 2+

Statistical comparison of a hybrid approach with approximate...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XVI

作者： Lee, K. David Wiesenfeld, Eric Gelfand, Andrew Decis Analyt Corp 1235 S Clark St Suite 400 Arlington VA 22202 USA

ISBN: (纸本)9780819466891

One of the greatest challenges in modem combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of realtime, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

关键词： fusion 2+ situation awareness Bayesian networks fuzzy logic junction tree algorithm min/max algorithm particle filtering algorithm

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