Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness

Bowman, Christopher; Haith, Gary; Steinberg, Alan N.; Morefield, C.; Morefield, Michael

doi:10.1117/12.2018386

Cited by 4 publications

(4 citation statements)

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“…Bowman, et al [9] expand on this by trying to improve the robustness of distributed fusion systems by incorporating ancillary data from non-traditional sources. They further argue that the entire information exploitation process can be viewed in terms of a functional duality between resource management levels and the inference levels as presented in Steinberg and Bowman's [10] revisions to the JDL data fusion model.…”

Section: Situationmentioning

confidence: 97%

An integrated model of hard and soft context in sensor management

Hintz

Kadar

2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

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The integration of hard (physical) and soft (meta-physical) contexts in an information fusion system requires the identification of the specific mission oriented goals which it is desired to achieve. Just as most sensors cannot acquire data omnidirectionally, it is not computationally feasible to evaluate all contexts within which acquired data can be understood by an information fusion system. We first define a notional problem consisting of operating and hiding areas and transit routes between them. We then define physical and meta-physical contexts within which data acquired from the observed area can be interpreted and define the piecewise application of context specific transformations to a partition of the global problem of understanding data in context.

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Section: Situationmentioning

confidence: 97%

An integrated model of hard and soft context in sensor management

Hintz

Kadar

2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

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“…In a companion paper, we describe how we have applied these ideas to the opportunistic use of "external" data; that is, data for which the system has not explicitly been designed to exploit [15].…”

Section: Adaptive Information Exploitationmentioning

confidence: 99%

“…Peirce's "abduction" -involves the integration of multiple models [15,16]. Thus, many biological phenomena are "explained" by integration of biology with chemistry.…”

Section: Data Fusion and Data Miningmentioning

confidence: 99%

Adaptive context exploitation

Steinberg

Bowman

2013

SPIE Proceedings

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This paper presents concepts and an implementation scheme to improve information exploitation processes and products by adaptive discovery and processing of contextual information.Context is used in data fusion -and in inferencing in general -to provide expectations and to constrain processing. It also is used to infer or refine desired information ("problem variables") on the basis of other available information ("context variables"). Contextual exploitation becomes critical in several classes of inferencing problems in which traditional information sources do not provide sufficient resolution between entity states or when such states are poorly or incompletely modeled.An adaptive evidence-accrual inference method -adapted from developments in target recognition and scene understanding -is presented; whereby context variables are selected on the basis of (a) their utility in refining explicit problem variables, (b) the probability of evaluating these variables to within a given accuracy, given candidate system actions (data collection, mining or processing), and (c) the cost of such actions. The Joint Directors of Laboratories (JDL) Data Fusion Model, with its extension to dual Resource Management functions, has been adapted to accommodate adaptive information exploitation, to include adaptive context exploitation. The interplay of Data Fusion and Resource Management (DF&RM) functionality in exploiting contextual information is illustrated in terms of the dual-node DF&RM architecture. An important advance is in the integration of data mining methods for data search/discovery and for abductive model refinement. OVERVIEWAs we stated in "Dirty secrets of data fusion" [1]: "Fusion [should not be] a static process: we've started at the wrong end and continue to focus on the wrong end." By this we mean that data fusion has traditionally been construed as a data-driven problem: do the best you can with whatever information is presented by sensors or other sources. Only recently has there been much realization of the importance of goal-drive, adaptive information exploitation: i.e. relating one's present information state to a desired information state and managing available resources to approach the desired state in a cost-effective manner.Our goal in this paper is to develop a model and an implementation scheme for seeking, discovering, selecting and fusing contextual information as part of a goal-driven information exploitation process. We show how such a method can be applied generally to information exploitation systems, such as ISR (Intelligence, Surveillance and Reconnaissance) systems.Section 2 provides a formal working definition for 'context' as applied to information exploitation. Section 3 considers the distinctive issues and the applicability of various techniques to diverse categories of information exploitation problems. Section 4 defines an implementation for adaptive evidence accrual and exploitation, to include goal-driven discovery and use of context. Section 5 considers the issues of adaptive evidenc...

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“…In [18], the author described the distributed fusion systems for integration. Solution includes adaptive methods that improve usefulness data sources.…”

Section: The Literature Reviewmentioning

confidence: 99%

ISR Data Processing in Military Operations

Buřita¹,

Novak²

2020

Adv. sci. technol. eng. syst. j.

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This paper provides an overview of Intelligence, Surveillance, and Reconnaissance (ISR) data with respect on NATO standards and recommendations; further presents methods, tools, and experiences in ISR data processing in military operations. The steps of the Intelligence cycle and disciplines Business Intelligence (BI), Data Warehousing, Data Mining, and Big Data are presented in the introduction. The literature review is oriented to the analysis of intersections between ISR and BI methods. The next chapter describes the ISR data processing in detail; there are listed structures, formats, standards, and data from the operational point of view. The ISR operational picture is explained, and steps of the ISR data mart is completed. The last part is oriented to Big Data processing; NoSQL, inmemory and streaming databases. The last two chapters are focused on the description of research results in the processing of ISR data. The ISR data mart experiment processes the radio transmission data that consists of detected radio signals. Results are visualized in RapidMiner Studio. The Big Data experiment is realized in Apache Hadoop.

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Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness

Cited by 4 publications

References 0 publications

An integrated model of hard and soft context in sensor management

An integrated model of hard and soft context in sensor management

Adaptive context exploitation

ISR Data Processing in Military Operations

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