Data Fusion Automation

Antony, Richard

doi:10.1201/9781420053098.ch7

Cited by 9 publications

(10 citation statements)

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“…[1][2]) and hierarchal methods to aid command and control (e.g. [3][4][5][6][7]). Target tracking estimates the geospatial position of entities over time.…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion using entropic measures of dependence

Deignan

2011

RMTA

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As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and continuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion.Keywords: Information theory; data association; fusion; estimation; entropy. ResumenContrario a los métodos estándar de asociación que ligan medidas de dispersión central, las medidas de entropía cuantifican relaciones multivaluadas. Esta distinción es especialmente importante cuando no existen modelos de alta fidelidad de los fenómenos detectados. Se muesrta que las propiedades de las medidas de entropía calzan en la marco Bayesiano de sensores jerárquicos de fusión. Se presenta un método de estimación de la estructura probabilística para medidas categóricas y continuas, el cual es insesgado para colecciones finitas de datos. Adicionalmente, se describe un método de ramificación y acotamiento de selecciónóptima del sensor apropiado tanto para refinamiento del objetivo como para detección de anomalías. Finalmente, la metodología es aplicada sobre un conjunto conocido de datos usados en una competencia est[andar de minería de datos, que caracteriza tanto descriptores ralos categóricos como continuos de un objetivo. Excelentes resultados cuantitativos y computacionales con estos datos apoyan la conclusión de que la metodología propuesta es promisoria para propósitos generales con datos bajos niveles de fusión.Palabras clave: Teoría de la información; datos de asociación; fusión; estimación; entropía.Mathematics Subject Classification: 94A17.

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“…[1][2]) and hierarchal methods to aid command and control (e.g. [3][4][5][6][7]). Target tracking estimates the geospatial position of entities over time.…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion using entropic measures of dependence

Deignan

2011

RMTA

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“…This is because no matter how people assign values to the positions, all the candidates will have the same score. 3 , p 2 , p 1 ), i.e., the order of the parameters is not significant. This means that the combined scores for the three candidates have the same value.…”

Section: Rankingmentioning

confidence: 94%

“…Order Fusion can be considered as a special case of Data Fusion [3], which plays a crucial role for capturing data representation and aggregation. As Data Fusion's foundations, a variety of researches on both mathematically formal methods (e.g., Bayesian, DempsterShafer, Fuzzy Logic) and somewhat ad hoc approaches (e.g., voting) have been studied for as long as hundreds of years.…”

Section: Introductionmentioning

confidence: 99%

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Raw relation sets and multiperson decision making

Chen

Meng

Proceedings. 15th IEEE International Conference on Tools With Artificial Intelligence

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“…Numerous multisensor data fusion architectures have been discussed in the literature [1][2][3][4][5][6][7][8][9][10]. A common feature of these architectures is that they incorporate multiple levels of data processing within the data fusion process.…”

Section: Introductionmentioning

confidence: 99%

Data fusion architecture for Maritime Surveillance

Gad

Farooq²

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

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Data Fusion Automation

Cited by 9 publications

References 0 publications

Sensor fusion using entropic measures of dependence

Sensor fusion using entropic measures of dependence

Raw relation sets and multiperson decision making

Data fusion architecture for Maritime Surveillance

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