On close-range air combat based on hidden Markov model

Theory of air combat (AC) develops from geometry to energy, the AC process is analyzed more in terms of the fighter performance; nevertheless, a small amount of literature considers how fighter pilot's decision-making process affects AC. According to the variable characteristics of objective data recorded in AC training, analysis method of close-range air combat (CRAC) based on hidden Markov model (HMM) is proposed, Viterbi algorithm is used to predict the changing process of pilot's state sequence in AC, then the decision-making point is acquired. Through simulation, the feasibility of AC based on HMM is verified, it also shows whether the pilot can make decisions effectively and fast will affects the AC result eventually.