The General Characteristics and Properties of a Fighter Jet Aircraft F-16

The fighting in the air-to-air combat and air-to-surface combat represents a relatively new type of combat, happening for a period of only around seventy years. In these years a quick technological advancement took place and caused the development of very primitive weapons to the present beyond the visual range or point-to-shoot capabilities of nowadays aircraft. Though aerial combat fundamentals remained very similar to those in the past, seasoned only by actual combat and exercises for training purposes. Although these standards remained almost untouched, the technological advancement in aircraft design and weapon systems is immense. This applies also to the fighter jet aircraft called F-16. The F-16 is one of the most widespread fighter jets ever produced, if not the most. It is important to improve the aircraft design and its systems for navigation, orientation, and maybe most importantly, its ‘weaponry and surrounding accessories.

Methodical approach to evaluating the effectiveness of electronic counter-countermeasures to airborne interference stations in aerial combat

Modern active jamming stations reduce the chance of aircraft getting hit by missiles with active and semi-active homing heads during aerial combat by 40-60%. The active development of airborne electronic countermeasures equipment forces to look for ways and means to not only protect against them, but also to actively counter these means, the so-called electronic counter-countermeasures. Nowadays, there are several methods of electronic counter-countermeasures to the enemy airborne active jamming stations, but the sequence and conditions of their application are not defined. Therefore, the paper proposes to determine the features of the application of three methods of electronic counter-countermeasures and to develop an algorithm for electronic counter-countermeasures against the enemy airborne active jamming stations in the conditions of creating polarization jamming of various kinds. The development of the algorithm allows to evaluate the efficiency of the application of electronic counter-countermeasures based on performance indicators of the functioning of an airborne targeting radar.

UAV Autonomous Aerial Combat Maneuver Strategy Generation with Observation Error Based on State-Adversarial Deep Deterministic Policy Gradient and Inverse Reinforcement Learning

With the development of unmanned aerial vehicle (UAV) and artificial intelligence (AI) technology, Intelligent UAV will be widely used in future autonomous aerial combat. Previous researches on autonomous aerial combat within visual range (WVR) have limitations due to simplifying assumptions, limited robustness, and ignoring sensor errors. In this paper, in order to consider the error of the aircraft sensors, we model the aerial combat WVR as a state-adversarial Markov decision process (SA-MDP), which introduce the small adversarial perturbations on state observations and these perturbations do not alter the environment directly, but can mislead the agent into making suboptimal decisions. Meanwhile, we propose a novel autonomous aerial combat maneuver strategy generation algorithm with high-performance and high-robustness based on state-adversarial deep deterministic policy gradient algorithm (SA-DDPG), which add a robustness regularizers related to an upper bound on performance loss at the actor-network. At the same time, a reward shaping method based on maximum entropy (MaxEnt) inverse reinforcement learning algorithm (IRL) is proposed to improve the aerial combat strategy generation algorithm’s efficiency. Finally, the efficiency of the aerial combat strategy generation algorithm and the performance and robustness of the resulting aerial combat strategy is verified by simulation experiments. Our main contributions are three-fold. First, to introduce the observation errors of UAV, we are modeling air combat as SA-MDP. Second, to make the strategy network of air combat maneuver more robust in the presence of observation errors, we introduce regularizers into the policy gradient. Third, to solve the problem that air combat’s reward function is too sparse, we use MaxEnt IRL to design a shaping reward to accelerate the convergence of SA-DDPG.