#Il2 air combat maneuvers software#

In, a genetics-based machine learning algorithm is implemented to generate high angle-of-attack air combat maneuver tactics for the X-31 fighter aircraft in a one-on-one air combat scenario. However, this approach is difficult to be applied in practice since the influence diagram is converted into a nonlinear programming problem, which cannot meet the demand of fast computation during air combat. Influence diagram is used to model the sequential maneuver decision in air combat, and high-performance simulation results are obtained. Other approaches for pursuit maneuver strategy generation include influence diagram, genetic algorithm, and approximate dynamic programming.

An optimal pursuit-evasion fighter maneuver is formulated as a differential game and then solved by nonlinear programming, which is complex and requires enormous amount of calculation. However, it is only suitable for rational opponents. This approach solves the problem that the matrix game cannot deal with continuous multiple states. In, the air combat game is regarded as the Markov game, and then the pursuit maneuver strategy is solved by computing its Nash equilibrium. formulated the cooperative occupancy decision-making problem in air combat as a zero-sum matrix game and designed the double-oracle combined algorithm with neighborhood search to solve the model. A limited search method is adopted over discrete maneuver choices to maximize a scoring function, and the feasibility of real-time autonomous combat is demonstrated in simulation. proposed a matrix game approach to generate intelligence maneuver decisions for one-on-one air combat. Game-theoretic-based approaches are widely used in the automation of air combat pursuit maneuver. presented a nonlinear, online model predictive controller for pursuit and evasion of two fixed-wing autonomous aircrafts, which rely on previous knowledge of the maneuvers. A virtual pursuit point-based combat maneuver guidance law for an unmanned combat aerial vehicle (UCAV) is presented and is used in X-Plane-based nonlinear six-degrees-of-freedom combat simulation. used rule-based dynamic scripting in one-on-one, two-on-one, and two-on-two air combat, which requires hard coding the air-combat tactics into a maneuver selection algorithm. In order to reduce the workload of pilots and remove the need to provide them with complex spatial orientation information, many research studies focus on the autonomous air combat maneuver decision. Pursuit is a kind of BFM, which aims to control an aircraft to reach a position of advantage when it is fighting against another aircraft.

Therefore, modern fighters are designed for close combat, and military pilots are trained in air combat basic fighter maneuvering (BFM). Introductionĭespite long-range radar and missile technology improvements, there is still a scenario that two fighter aircrafts may not detect each other until they are within the visual range. In a competition with all opponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing is about 75%. For the training of an opponent with the adaptive strategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical angle fight tactics can be learnt by the deep reinforcement learning agents. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive strategies. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal with nonstationarity. A reward shaping approach is used, by which the training speed is increased, and the performance of the generated trajectory is improved.

#Il2 air combat maneuvers software#

Middleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning environment for agent training. The maneuver strategy agents for aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario. In this paper, an alternate freeze game framework based on deep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. In a one-on-one air combat game, the opponent’s maneuver strategy is usually not deterministic, which leads us to consider a variety of opponent’s strategies when designing our maneuver strategy.