Measures of effectiveness for high-level fusion

Blasch, Erik; Valin, Pierre; Bossé, Éloi

doi:10.1109/icif.2010.5711858

Cited by 92 publications

(60 citation statements)

References 42 publications

(40 reference statements)

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“…Dismount tracking requires pragmatic assessment of the context, culture, and situations [74,75]. With the advent of current information fusion technology, there is a need to augment tools with information to support timely and actionable decisions [76].…”

Section: Discussionmentioning

confidence: 99%

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Dismount tracking and identification from electro-optical imagery

Blasch

Ling

et al. 2012

Evolutionary and Bio-Inspired Computation: Theory and Applications VI

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With the advent of new technology in wide-area motion imagery (WAMI) and full-motion video (FMV), there is a capability to exploit the imagery in conjunction with other information sources for improving confidence in detection, tracking, and identification (DTI) of dismounts. Image exploitation, along with other radar and intelligence information can aid decision support and situation awareness. Many advantages and limitations exist in dismount tracking analysis using WAMI/FMV; however, through layered management of sensing resources, there are future capabilities to explore that would increase dismount DTI accuracy, confidence, and timeliness. A layered sensing approach enables commandlevel strategic, operational, and tactical analysis of dismounts to combine multiple sensors and databases, to validate DTI information, as well as to enhance reporting results. In this paper, we discuss WAMI/FMV, compile a list of issues and challenges of exploiting the data for WAMI, and provide examples from recently reported results. Our aim is to provide a discussion to ensure that nominated combatants are detected, the sensed information is validated across multiple perspectives, the reported confidence values achieve positive combatant versus non-combatant detection, and the related situational awareness attributes including behavior analysis, spatial-temporal relations, and cueing are provided in a timely and reliable manner to stakeholders.

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Section: Discussionmentioning

confidence: 99%

“…Future performance evaluation requires situation analysis [68] that leads to analysis of both measures of performance and effectiveness [69].…”

Section: Layered Sensing Dismount Tracking Directionsmentioning

confidence: 99%

Dismount tracking and identification from electro-optical imagery

Blasch

Ling

et al. 2012

Evolutionary and Bio-Inspired Computation: Theory and Applications VI

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“…Situation and Knowledge Representations [16,17] HLIF system design [18,19] HLIF for decision support [20] HLIF evaluation [21,22] Three common themes throughout the papers include: (A) Information fusion designs support situational awareness. Advanced techniques in design (e.g.…”

Section: Fusion10 Panel Papers Overviewmentioning

confidence: 99%

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

Blasch

Llinas

Lambert

et al. 2010

2010 13th International Conference on Information Fusion

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-The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary HLIF advances and developments to determine unsolved grand challenges and issues. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and high-level information fusion (control, situational understanding, and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), systems design (scenario-based, user-based, distributedagent) The HLIF panel discussion's goal is to highlight the unsolved problems and concerns to motivate the information fusion community towards systems-level solutions. The panelists' expert perspectives are based on three areas: (1) previous panel discussions and summaries, (2) an integrated list of HLIF challenges, and (3) companion papers presented at the Fusion2010 conference (note we switch to Fusion10 to refer to the conference). Previous Related Panel DiscussionsPanel discussions provide a valuable resource to the community to overview the current techniques and provide areas of concern for future research. Previous Fusion Conference panel discussion papers related to HLIF include knowledge representation (Fusion05) [7], resource management coordination with situation and threat assessment (Fusion06) [8, 9, 10], agent-based design (Fusion07) [11], and HLIF challenges (Fusion08) [12]. Three panel discussions were conducted at Fusion09 without papers: Many of the authors of this Fusion10 HLIF panel coordinated on previous publications, but continual refinement of HLIF contemporary are desired. The panel discussion follows from a day-long event special session. There are most likely other papers at Fusion10 that are related that would validate good questions from the audience to the panelists. Many of the participants to the special session would be encouraged to voice their opinions and questions to the moderated panel.

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“…Typically object assessment algorithms track and identify the object to providing measures of performance (MOPs). MOPs support the measures of effectiveness, which can vary over the sensor types, environmental conditions, targets of interest, situational context, and users [36]. One example of MOVINT is detecting cars moving in an urban area [37].…”

Section: Information Fusion Decision Supportmentioning

confidence: 99%

“…In this paper, we are concerned with the decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. In [3], we postulated a general High Level Information Fusion (HLIF) MOE as: Effectiveness = InfoGain * Quality * Robustness…”

Section: Introductionmentioning

confidence: 99%

Information fusion measures of effectiveness (MOE) for decision support

2011

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For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

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Measures of effectiveness for high-level fusion

Cited by 92 publications

References 42 publications

Dismount tracking and identification from electro-optical imagery

Dismount tracking and identification from electro-optical imagery

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

Information fusion measures of effectiveness (MOE) for decision support

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