Evidential force aggregation

Schubert, Johan

doi:10.1109/icif.2003.177377

Cited by 20 publications

(7 citation statements)

References 12 publications

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“…• There is a conflict between the bbas reported by two reliable sources of beliefs. • There is a positive belief that the two sources of bbas do not report on the same object [70,71,74,9,76] (see details in Sections 5.2 and 6.2.3).…”

Section: The Tbm Casementioning

confidence: 95%

Analyzing the combination of conflicting belief functions

2007

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We consider uncertain data which uncertainty is represented by belief functions and that must be combined. The result of the combination of the belief functions can be partially conflictual. Initially Shafer proposed Dempster's rule of combination where the conflict is reallocated proportionally among the other masses. Then Zadeh presented an example where Dempster's rule of combination produces unsatisfactory results. Several solutions were proposed: the TBM solution where masses are not renormalized and conflict is stored in the mass given to the empty set, Yager's solution where the conflict is transferred to the universe and Dubois and Prade's solution where the masses resulting from pairs of conflictual focal elements are transferred to the union of these subsets. Many other suggestions have then been made, creating a 'jungle' of combination rules. We discuss the nature of the combinations (conjunctive versus disjunctive, revision versus updating, static versus dynamic data fusion), argue about the need for a normalization, examine the possible origins of the conflicts, determine if a combination is justified and analyze many of the proposed solutions.

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Section: The Tbm Casementioning

confidence: 95%

Analyzing the combination of conflicting belief functions

2007

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“…Currently, there is a need for systems to determine the intent observation [16,17], threat prediction [18], situational awareness [19], situational analysis [20], situational prediction [21], force aggregation [22], cyber analysis [23], sensor placement [24], and political analysis [25]; all of which is a holistic interpretation of fusion systems [26] and events.…”

Section: Introductionmentioning

confidence: 99%

Measures of effectiveness for high-level fusion

Blasch

Valin

Bossé

2010

2010 13th International Conference on Information Fusion

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Current advances in technology, sensor collection, data storage, and data distribution have afforded more complex, distributed, and operational information fusion systems (IFSs). IFSs notionally consist of low-level (data collection, registration, and association in time and space) and high-level fusion (user coordination, situational awareness, and mission control). Low-level IFSs typically rely on standard metrics for evaluation such as timeliness, accuracy, and confidence. Given the broader use of IFSs, it is also important to look at high-level fusion processes and determine a set of metrics to test IFSs, such as workload, throughput, and cost. Three types of measures (measures of performance MOP, measures of effectiveness MOE, and measures of merit MOM) are summarized. In this paper, we seek to describe MOEs for High-Level Fusion (HLF) based on developments in Quality of Service (QOS) and Quality of Information (QOI) that support the user and the machine, respectively. We define a HLF MOE based on (1) information quality, (2) robustness, and (3) information gain. We demonstrate the HLF MOE based for a maritime domain situation awareness example.

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“…For example, Lowrance et al apply Dempster-Shafer theory in reasoning about the locations and activities of multiple ships from intelligence reports [43]; Bogler studies whether the targets belong to the set of friendly or the set of not friendly aircraft in multi-sensor target identification systems using Dempster-Shafer theory [14]. Recently, Schubert extends those approaches to force clustering and classification [50,51], where elements, e.g. intelligence reports, vehicles, and echelons, are clustered into subsets.…”

Section: Sensor Data Fusion and Force Structure Recognitionmentioning

confidence: 99%