An information fusion demonstrator for tactical intelligence processing in network-based defense

Ahlberg, Simon; Hörling, Pontus; Johansson, Katarina; Jöred, Karsten; Mårtenson, Christian; Neider, Göran; Schubert, Johan; Svenson, Pontus; Svensson, Per; Walter, Johan

doi:10.1016/j.inffus.2005.11.002

Cited by 30 publications

(16 citation statements)

References 32 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of its importance, the orientation has been researched on for several decades in forms of intelligence processing [Ahlberg et al, 2007] and situational awareness [Endsley and Garland, 2000].…”

Section: "The Second O Orientation -As the Repository Of Our Geneticmentioning

confidence: 99%

Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction

Xu¹,

Xiao

Yin

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

Goal recognition, which is the task of inferring an agent's goals given some or all of the agent's observed actions, is one of the important approaches in bridging the gap between the observation and decision making within an observe-orient-decide-act cycle. Unfortunately, few research focuses on how to improve the utilization of knowledge produced by a goal recognition system. In this work, we propose a Markov Decision Process-based goal recognition approach tailored to a dynamic shortest-path local network interdiction (DSPLNI) problem. We first introduce a novel DSPLNI model and its solvable dual form so as to incorporate real-time knowledge acquired from goal recognition system. Then a Markov Decision Process-based goal recognition model along with its dynamic Bayesian network representation and the applied goal inference method is proposed to identify the evader's real goal within the DSPLNI context. Based on that, we further propose an efficient scalable technique in maintaining action utility map used in fast goal inference, and develop a flexible resource assignment mechanism in DSPLNI using knowledge from goal recognition system. Experimental results show the effectiveness and accuracy of our methods both in goal recognition and dynamic network interdiction.

show abstract

Section: "The Second O Orientation -As the Repository Of Our Geneticmentioning

confidence: 99%

Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction

Xu¹,

Xiao

Yin

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…These optimisation algorithms fall into two categories: mathematical programming [19,[26][27][28] and artificial intelligent search techniques [29,30]. Simulation techniques are another possibility, where possible future timelines are investigated using multiple trials [31][32][33]. Sometimes random approaches are followed and these are typically used as an electronic countermeasure [34].…”

Section: Current Scheduling Solutionsmentioning

confidence: 99%

Interval Algebra – An effective means of scheduling surveillance radar networks

2015

View full text Add to dashboard Cite

Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion Information Group model of information fusion. In this paper, the use of Interval Algebra to schedule mechanically steered radars to make multistatic measurements for selected targets of importance is shown.Interval Algebra provides a framework for incorporating a richer set of requirements, without requiring modifications to the underlying algorithms.The performance of Interval Algebra was compared to that of the Greedy Randomised Adaptive Search Procedure and the applicability of Interval Algebra to nimble scheduling was investigated using Monte-Carlo simulations of a binary radar system. The comparison was done in terms of actual performance as well as in terms of computation time required. The performance of the algorithms was quantified by keeping track of the number of targets that could be measured simultaneously. It was found that nimble scheduling is important where the targets are moving fast enough to rapidly change the recognised surveillance picture during a scan.Two novel approaches for implementing Interval Algebra for scheduling surveillance radars are presented. It was found that adding targets on the fly and improving performance by incrementally growing the network is more efficient than pre-creating the full network. The second approach stemmed from constraint ordering. It was found that for simple constraint sets, the Interval Algebra relationship matrix reduces to a single vector of interval sets.The simulations revealed that an Interval Algebra algorithm that utilises both approaches can perform as well as the Greedy Randomised Adaptive Search Procedure with similar processing time requirements. Finally, it was found that nimble scheduling is not required for surveillance radar networks where ballistic and supersonic targets can be ignored. Nevertheless, Interval Algebra can easily be used to perform nimble scheduling with little modification and may be useful in scheduling the scans of multifunction radars.

show abstract

“…In other words, when a system is automated it might perform operations which the user is unaware off, hence, the user's situation awareness decreases. Nevertheless, automating the information fusion process is still foremost a research issue [26]. Many researchers have considered the technical aspects, however, the users of the system are the ones making the decisions, and therefore, they are an essential part when automating the information fusion process.…”

Section: Tackling Problems Outside-inmentioning

confidence: 99%

Rethinking Level 5: Distributed Cognition and Information Fusion

Nilsson

Ziemke

2006

2006 9th International Conference on Information Fusion

View full text Add to dashboard Cite

-The focus of most information fusion research, so far, has been on the technology, i.e. information processing in machines. However, the importance of also understanding human information processing, and the interaction between humans and machines, is gaining increasing recognition. This position paper argues that a distributed cognition perspective, which considers cognitive processes not as taking place solely inside people's heads, but as distributed over human thought processes and the material, social and organisational environment they are embedded in, may help to provide a deeper understanding of information fusion processes.

show abstract

An information fusion demonstrator for tactical intelligence processing in network-based defense

Cited by 30 publications

References 32 publications

Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction

Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction

Interval Algebra – An effective means of scheduling surveillance radar networks

Rethinking Level 5: Distributed Cognition and Information Fusion

Contact Info

Product

Resources

About