Abstract
Simulation and modelling of surface-to-air missile defense (or surface based air defense – SBAD) is demanding process if we want to encompass most important capabilities of the force. There are certain aspects that need to be taken into account to create realistic models and simulation of SBAD forces exploitable not only in simulators, but also in the future SBAD weapon platforms with autonomous modes of operation. For example, most of today’s simulators do not provide realistic simulation of surfaced-to-air missile (SAM) systems in the matter of implemented guidance methods mathematic models, effective engagement envelope calculation, cluster tactics algorithms, etc. That is the reason why e.g. tactical simulators for jets pilots training provide just simple SAM models with low-level artificial intelligence and pilots are not prepared for real SAM systems behavior. These simulators do not have any sophisticated artificial intelligence and do not use advanced autonomous modes to challenge the trained pilots’ skills. This paper deals with these aspects and offers ways, how to model key SBAD capabilities to train jet pilots effectively against this type of threat.
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Notes
- 1.
Researched during the period 2014–2017 at the University of Defence, Department of Air Defence Systems, Czech Republic. DZRO project and DADS Specific research.
- 2.
Or Digital Terrain Model (DTM), Digital Elevation Model (DEM), however, the DSM includes also human made obstacles so for the SAM firing simulation is more suitable.
- 3.
The missile speed vector direction is always aimed on the target.
- 4.
On the Fig. 8, the scale on axes is deleted intentionally.
- 5.
Air Defence Systems Department at University of Defence in Brno, and Military Technical Institute, Czech Republic is starting a subsequent research in the field of SAM system modelling implementation into the tactical simulators in 2018.
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Farlik, J., Tesar, F. (2018). Aspects of the Surface-to-Air Missile Systems Modelling and Simulation. In: Mazal, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2017. Lecture Notes in Computer Science(), vol 10756. Springer, Cham. https://doi.org/10.1007/978-3-319-76072-8_23
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