Susceptibility of Sensors to IEMI Attacks

Swarm UAV Defeat Modelling through Lifetime Distribution Analysis

“…Note that since the time-varying component R i (t) in the right hand side of ( 7) is independent of j, it may be removed from the summation. This is useful from the point of view of developing a simpler statistical expression for the complementary distribution function of the lifetime of the ith UAV, as defined in the left hand side of (7). The inverse cumulative distribution function method can be used to simulate L i from (7) as follows.…”

“…and the solution to (8) in terms of t will be a realisation of L i . By applying (7) to (8) one can show that the solution is given by…”

“…Corresponding disruption thresholds are modelled by exponential random variables with means of 1000, 175, and 25 W/m 2 for the first class of UAVs and 1000, 150, and 100 W/m 2 for the second. These model parameterisations have been motivated by discussions in [7,[13][14][15]. The inherent target surface area vulnerability (A R in (1)) is selected arbitrarily ranging from 0.05 m 2 to 0.1 m 2 .…”

“…Hence, to understand the minimum power requirements in order to achieve a disruptive effect at a given range, the formulae introduced in the previous section can be utilised. Consider a typical member of both classes of UAVs individually, reducing their lifetimes given by (7), to each of the vulnerabilities. If a disruptive effect is desired at a range of 250 m, then the susceptibility of each vulnerability and the UAV as a whole can be determined.…”

“…Such systems couple electromagnetic energy into their targets, and thus can cause a number of system failures [5]. As such, there has been much interest in quantifying the effects of HPRF DEWs when being applied to counter UAV applications [6][7][8]. Stochastically-driven models for HPRF DEW performance prediction in this context have also been introduced in recent years [9,10].…”