Risk management with hard-soft data fusion in maritime domain awareness

Falcón, Rafael; Abielmona, Rami; Billings, Sean; Plachkov, Alex; Abbass, Hussein A.

doi:10.1109/cisda.2014.7035641

Cited by 19 publications

(11 citation statements)

References 11 publications

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“…Recent trends include hard-soft data fusion [44], behavioral learning for situation awareness [45], and situation/threat visualization for a user-defined operating picture [46].…”

Section: Situation/impact Awarenessmentioning

confidence: 99%

One decade of the Data Fusion Information Group (DFIG) model

Blasch

2015

SPIE Proceedings

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The revision of the Joint Directors of the Laboratories (JDL) Information Fusion model in 2004 discussed information processing, incorporated the analyst, and was coined the Data Fusion Information Group (DFIG) model. Since that time, developments in information technology (e.g., cloud computing, applications, and multimedia) have altered the role of the analyst. Data production has outpaced the analyst; however the analyst still has the role of data refinement and information reporting. In this paper, we highlight three examples being addressed by the DFIG model. One example is the role of the analyst to provide semantic queries (through an ontology) so that vast amount of data available can be indexed, accessed, retrieved, and processed. The second idea is reporting which requires the analyst to collect the data into a condensed and meaningful form through information management. The last example is the interpretation of the resolved information from data that must include contextual information not inherent in the data itself. Through a literature review, the DFIG developments in the last decade demonstrate the usability of the DFIG model to bring together the user (analyst or operator) and the machine (information fusion or manager) in a systems design.

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“…Recent trends include hard-soft data fusion [44], behavioral learning for situation awareness [45], and situation/threat visualization for a user-defined operating picture [46].…”

Section: Situation/impact Awarenessmentioning

confidence: 99%

One decade of the Data Fusion Information Group (DFIG) model

Blasch

2015

SPIE Proceedings

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“…Recently, the authors augmented the situational awareness capabilities of the RMF through the incorporation of both hard (i.e., structured) and soft (i.e., unstructured) data sources within maritime surveillance scenarios [11]. Risk features pertaining to maritime vessels were defined a priori and then quantified in real time using hard-soft data fusion in order to characterize and evaluate potential vessels in distress.…”

Section: The Risk Management Frameworkmentioning

confidence: 99%

“…1, this is a risk-driven analysis. We proceed to define the risk features in Table I, where φ BAD-WEATHER (X) is a function of how harsh the atmospheric and marine conditions in the area around vessel X are, φ ZONES (X) is a function of the number, type and strategic importance of the critical infrastructures in the area near vessel X and φ NR-INCIDENTS (X) is the number of maritime incidents reported in the area around X (see [11]). …”

Section: ) Ais Turned Off Anomaly Detectionmentioning

confidence: 99%

Risk-driven intent assessment and response generation in maritime surveillance operations

Falcón

Abielmona

Billings

2015

2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision

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Decision support systems (DSSs) are playing an increasingly important role in the characterization of suspicious activities in an area of interest given their proved ability to turn vast amounts of raw data into actionable intelligence that is easy to understand by human operators. Although risk management is an integral component of the decision making process that directly contributes towards improved situational awareness and response assessment, an active end-to-end consideration of the underlying risk sources in the environment is still an important feature that most DSSs currently lack. Additionally, deciding on an appropriate course of action (COA) to mitigate emerging threats in the system is a challenging task even for domain experts given that (1) the number of potential responses to analyze could be overwhelmingly large; (2) seldom are those responses judged in terms of the risks associated with their enactment and (3) assessing the effectiveness of the potential responses in the real world is usually time-consuming and simulation-driven.In this paper, we formalize the adaptation of a recently proposed Risk Management Framework to account for behavioral intents associated with the objects of interest (OOIs) in the monitoring environment and their link to automatic response generation. The intent of the objects is inferred from high-level cognitive and behavioral knowledge in the form of anomalies. When an OOI has crossed a permissible risk threshold, we demonstrate how responses to that situation can be automatically elicited by the COA recommendation module of a risk-aware DSS. Multicriteria decision analysis (MCDA) is used to judge a diverse set of plausible responses according to different operational objectives. We illustrate the application of the proposed framework in the context of maritime surveillance operations by triggering a corporate search for a missing vessel. To the best of our knowledge, this is the first time that risk features are synthesized from anomalies and integrated into a more comprehensive RMF engine for knowledge (response) elicitation.Index Terms-situational awareness; risk management; intent and threat assessment; course of action recommendation; decision support systems; multicriteria decision making; high-level information fusion

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“…Most discussions of multi-intelligence tools require a situation description [13], ontology [14], evaluation support [15], relation to threats [16], and risks [17]. Three types of situation discussions are needed to provide assessment (machine), awareness (user), and understanding (users-machines teaming) [18], as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

Blasch

2015

SPIE Proceedings

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Assessment of multi-intelligence fusion techniques includes credibility of algorithm performance, quality of results against mission needs, and usability in a work-domain context. Situation awareness (SAW) brings together low-level information fusion (tracking and identification), high-level information fusion (threat and scenario-based assessment), and information fusion level 5 user refinement (physical, cognitive, and information tasks). To measure SAW, we discuss the SAGAT (Situational Awareness Global Assessment Technique) technique for a multi-intelligence fusion (MIF) system assessment that focuses on the advantages of MIF against single intelligence sources. Building on the NASA TLX (Task Load Index), SAGAT probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points; we highlight these tools for use in a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measures of product quality and QuEST of information. Building a notion of product quality from multiintelligence tools is typically subjective which needs to be aligned with objective machine metrics.

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Risk management with hard-soft data fusion in maritime domain awareness

Cited by 19 publications

References 11 publications

One decade of the Data Fusion Information Group (DFIG) model

One decade of the Data Fusion Information Group (DFIG) model

Risk-driven intent assessment and response generation in maritime surveillance operations

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

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