| 3231. |
- Johansson, Daniel, et al.
(författare)
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Design and development of an augmented environment with high user mobility for training purposes
- 2008
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Ingår i: Mechatronics 2008. - Limerick, Ireland : University of Limerick. - 9781905952052 - 1905952058
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Konferensbidrag (refereegranskat)abstract
- The paper describes the design and development of a novel cost effective simulator for training of situation awareness, strategy and co-operation. By mixing real and virtual realities in combination with wireless and body-mounted hardware, the result is an augmented environment that allows for high physical mobility against a relatively low cost.
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| 3232. |
- Johansson, Daniel, et al.
(författare)
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Omnidirectional Robotic Telepresence through Augmented Virtuality for Increased Situation Awareness in Hazardous Environments
- 2009
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Ingår i: Systems, Man and Cybernetics. - : IEEE. - 9781424427949 - 9781424427932 ; , s. 6-11
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel low-cost robotic telepresence approach to situation awareness, initially aimed for hazardous environments. The robot supports omnidirectional movement, wide field of vision, haptic feedback and binaural sound. It is controlled through an augmented virtuality environment with an intuitive position displacement scheme that supports physical mobility. The operator thereby can conduct work away from danger whilst retaining situation awareness of the real environment.
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| 3233. |
- Johansson, Fredrik, et al.
(författare)
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A Bayesian network approach to threat evaluation with application to an air defense scenario
- 2008
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Ingår i: Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, Cologne, 30 June 2008–3 July 2008. - : IEEE Computer Society. - 9783000248832 - 9783800730926 ; , s. 1352-1358
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Konferensbidrag (refereegranskat)abstract
- In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have been developed. The system is based on a Bayesian network approach, making it possible to handle imperfect observations. The structure of the Bayesian network is described in detail. Finally, an analysis of the system’s performance as applied to a synthetic scenario is presented.
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| 3234. |
- Johansson, Fredrik, et al.
(författare)
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A Comparison between Two Approaches to Threat Evaluation in an Air Defense Scenario
- 2008
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Ingår i: Modeling Decisions for Artificial Intelligence. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540882688 - 9783540882695 ; , s. 110-121
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Konferensbidrag (refereegranskat)abstract
- Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.
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| 3235. |
- Johansson, Fredrik, et al.
(författare)
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A Suite of Metaheuristic Algorithms for Static Weapon-Target Allocation
- 2010
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Ingår i: GEM 2010. - : CSREA Press. - 1601321457 ; , s. 132-138
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Konferensbidrag (refereegranskat)abstract
- The problem of allocating defensive weapon resources to hostile targets is an optimization problem of high military relevance. The need for obtaining the solutions in real-time is often overlooked in existing literature. Moreover, there does not exist much research aimed at comparing the performance of different algorithms for weapon-target allocation. We have implemented a suite of metaheuristic algorithms for solving the static weapon-target allocation problem, and compare their real-time performance on a large set of problem instances using the open source testbed SWARD. The compared metaheuristic algorithms are ant colony optimization, genetic algorithms, and particle swarm optimization. Additionally, we have compared the quality of the generated allocations to those generated by a well-known maximum marginal return algorithm. The results show that the metaheuristic algorithms perform well on small- and medium-scale problem sizes, but that real-time requirements limit their usefulness for large search spaces.
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| 3236. |
- Johansson, Fredrik, et al.
(författare)
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A survivability-based testbed for comparing threat evaluation algorithms
- 2008
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Ingår i: Proceedings of the 2nd Skövde Workshop on Information Fusion Topics (SWIFT 2008). - Skövde : University of Skövde. - 9789163336973 ; , s. 22-24
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Konferensbidrag (refereegranskat)abstract
- Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target’s velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe how threat evaluation algorithms can be compared to each other, using a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets).
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| 3237. |
- Johansson, Fredrik, et al.
(författare)
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A testbed based on survivability for comparing threat evaluation algorithms
- 2009
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Ingår i: Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing. - : SPIE. - 9780819476180 ; , s. Article ID 73520C-
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Konferensbidrag (refereegranskat)abstract
- Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target's velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe an implemented testbed in which threat evaluation algorithms can be compared to each other, based on a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets). Our obtained results for two different threat evaluation algorithms are presented and analyzed.
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| 3238. |
- Johansson, Fredrik, et al.
(författare)
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An empirical investigation of the static weapon-target allocation problem
- 2009
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Ingår i: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009). - Skövde : University of Skövde. - 9789197851329 ; , s. 63-67
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Konferensbidrag (refereegranskat)abstract
- The allocation of weapons to targets (such as missiles and hostile aircrafts) is a well-known resource allocation problem within the field of operations research. It has been proven that this problem, in general, is NP-complete. For this reason, optimal solutions to the static weapon-target allocation (WTA) problem can not be obtained in real-time for large-scale problems. We try to find the limit for how large problems that can be solved optimally in real-time by exhaustive search algorithms through running empirical experiments. We also propose a heuristic genetic algorithm for solving larger-scale problems.
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| 3239. |
- Johansson, Fredrik, et al.
(författare)
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Detection of vessel anomalies : A Bayesian network approach
- 2007
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Ingår i: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007). - : IEEE Computer Society. - 1424415020 - 9781424415021 - 9781424415014 ; , s. 395-400
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Konferensbidrag (refereegranskat)abstract
- In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.
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| 3240. |
- Johansson, Fredrik
(författare)
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Evaluating the performance of TEWA systems
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- It is in military engagements the task of the air defense to protect valuable assets such as air bases from being destroyed by hostile aircrafts and missiles. In order to fulfill this mission, the defenders are equipped with sensors and firing units. To infer whether a target is hostile and threatening or not is far from a trivial task. This is dealt with in a threat evaluation process, in which the targets are ranked based upon their estimated level of threat posed to the defended assets. Once the degree of threat has been estimated, the problem of weapon allocation comes into the picture. Given that a number of threatening targets have been identified, the defenders need to decide on whether any firing units shall be allocated to the targets, and if so, which firing unit to engage which target. To complicate matters, the outcomes of such engagements are usually stochastic. Moreover, there are often tight time constraints on how fast the threat evaluation and weapon allocation processes need to be executed. There are already today a large number of threat evaluation and weapon allocation (TEWA) systems in use, i.e. decision support systems aiding military decision makers with the threat evaluation and weapon allocation processes. However, despite the critical role of such systems, it is not clear how to evaluate the performance of the systems and their algorithms. Hence, the work in thesis is focused on the development and evaluation of TEWA systems, and the algorithms for threat evaluation and weapon allocation being part of such systems. A number of algorithms for threat evaluation and static weapon allocation are suggested and implemented, and testbeds for facilitating the evaluation of these are developed. Experimental results show that the use of particle swarm optimization is suitable for real-time target-based weapon allocation in situations involving up to approximately ten targets and ten firing units, while it for larger problem sizes gives better results to make use of an enhanced greedy maximum marginal return algorithm, or a genetic algorithm seeded with the solution returned by the greedy algorithm.
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