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2023-04-10
Optimisation of Directed Energy Systems' Positions Subject to Uncertainty in Operations
By
Progress In Electromagnetics Research Letters, Vol. 110, 47-53, 2023
Abstract
Directed energy weapons (DEWs) have been identified as valuable assets in future land and joint combat. High-power radio frequency (HPRF) is a form of DEW which can neutralise robotic systems by discharging electromagnetic (EM) radiation over a region to couple system electronics. Its widespread effect enables the simultaneous disruption of groups of electronic systems, such as swarms of unmanned aerial systems (UASs). Since EM radiation is a distance-based effect, the arrangement of defensive HPRF systems with respect to their target is critical to understanding their utility and viability. Consequently, a mathematical model to assess the effectiveness of HPRF DEW positioned at a given location is formulated. Towards this, a combat scenario specialised to land operations is defined. The assumptions required to formulate the scenario geometrically and mathematically are also outlined. Provided with the position of an effector, it is then possible to quantify the vulnerability of a UAS swarm in terms of a disruption probability. This accounts for uncertainty stemming from UAS and swarm behaviour and assumes that UASs are independent and identically distributed. The model also draws upon work previously conducted at Defence Science Technology Group (DSTG) which derived an HPRF disruption probability function. An optimisation of the disruption probability is undertaken in terms of the position of a single narrowband HPRF effector. Under a hypothesised set of HPRF and threat parameters, maximal swarm defeat probabilities are examined in different swarm deployment regions and HPRF beam widths. This led to the discovery of various tradeoffs between aforementioned features. In particular, under a fixed beam width, proximity to the swam provided an increased defeat probability but reduced the beam's coverage of the swarm. Hence, numerous UASs might not be affected by EM radiation throughout the engagement, reflected in a reduction to the swarm defeat probability.
Citation
Mitchell Kracman, "Optimisation of Directed Energy Systems' Positions Subject to Uncertainty in Operations," Progress In Electromagnetics Research Letters, Vol. 110, 47-53, 2023.
doi:10.2528/PIERL23022208
References

1. Nielson, P. E., Effects of Directed Energy Weapons, National Defence University, Washington, 1994.

2. Cheraghi, A., S. S. Reza, and G. Kalman, "Past, present, and future of swarm robotics," Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys), Vol. 3, 190-233, 2022.

3. Swarms, I., Autonomous Drone Swarms, www.icarusdroneswarms.com/.

4. DARPA Offensive Swarm-Enabled Tactics, www.darpa.mil/work-with-us/offensive-swarm-enabled-tactics.

5. Epirus Leonidas Counter-Electronics, www.epirusinc.com/counter-electronics.

6. Weinberg, G. V., "Prediction of UAV swarm defeat with high-power radio frequency fields," IEEE Transactions on Electromagnetic Compatibility, Vol. 64, No. 6, 2157-2162, 2022.
doi:10.1109/TEMC.2022.3193881

7. Gao, F. and L. Han, "Implementing the nelder-mead simplex algorithm with adaptive parameters," Computational Optimization and Applications, Vol. 51, No. 1, 259-277, 2022.
doi:10.1007/s10589-010-9329-3

8. Dowsland, K. A. and J. Thompson, "Simulated annealing," Handbook of Natural Computing, 1623-1655, 2012.
doi:10.1007/978-3-540-92910-9_49