A Methodology for Missile Countermeasures Optimization under Uncertainty

Abstract

The missile countermeasures optimization problem is a complex strategy optimization problem that combines aircraft maneuvers with additional countermeasures in an attempt to survive attack from a single surface-launched, anti-aircraft missile. Classic solutions require the evading aircraft to execute specific sequences of maneuvers at precise distances from the pursuing missile and do not effectively account for uncertainty about the type and/or current state of the missile. This paper defines a new methodology for solving the missile countermeasures optimization problem under conditions of uncertainty. The resulting genetic programming system evolves programs that combine maneuvers with such countermeasures as chaff, flares, and jamming to optimize aircraft survivability. This methodology may be generalized to solve strategy optimization problems for intelligent, autonomous agents operating under conditions of uncertainty.

Evolving cooperative strategies for UAV teams

“…GP has also been used to evolve control strategies in single-aircraft scenarios. Moore [12] evolved effective behaviors for missile avoidance on manned jets, while Barlow et al [3] have evolved controllers for locating a radar source in a single-UAV domain. However, there is a lack of research exploring the use of GP for evolving cooperative strategies in realistic multi-UAV domains.…”

A genetic programming formulation to evade proportional navigation

“…certainty of the time of fire of the missile(s) but uncertainty concerning the location of fire of the missile(s). The same technique and software might be used to satisfy quite different requirements, providing a solution over a sliding scale of uncertainty [17]. Moreover, an evasion policy is required that can ensure high probability of survival in two extreme cases: (j) against many different types of missiles or (k) against a specific type of missile.…”

“…This paper applies the steady state version of Genetic Programming with tournament selection [7] to evolve the evasion of an aircraft from a missile that pursues it. An objective of this work is to present computations that can be easily validated and rules that can be easily interpreted unlike those in [17]. GP works by incremental improvement or evolution in the evasion capability of a set or population of evasion policies.…”

High Performance Evolutionary Computation

“…For example, Moore [16] describes a missile counter-measure problem where a strategy was evolved to evade an anti-aircraft missile. In this problem, a complex strategy of countermeasures optimization is investigated.…”