## 1 Introduction  
As an emerging combat force, UCAVs are playing an increasingly important role in warfare. With its excellent performance advantages such as higher agility, harder overload durability, and higher stealth capability, UCAVs gradually develop rapidly towards the direction of direct attack and killing ability [1]. Currently, the UCAV is mainly applied for airborne intelligence, surveillance and reconnaissance, close air support, and electronic support measure in actual combat missions. Due to the limitations of its situational awareness and intelligent level, it has not yet formed the capability of direct air combat, and it is difficult to replace the manned fighter in the intricate dogfight (i.e., short-range air combat) with high-dynamic and intense confrontation [2; 3]. Therefore, this paper studies its application in BVR air combat from the perspective of realizability. As the main form of fighting for air superiority at present, BVR air combat has a relatively moderate confrontation rhythm. The mode that the UCAV serves as an aerial \"shooter\" in the BVR air combat can avoid the intricate dogfight, in which mode the UCAV can adopt advanced BVR AAMs as airborne weapons and follows the tactical guidelines of \"first detection, first shot, first hit and first leave\". Advanced tactical data link and airborne avionics also laid the foundation for this air combat moderef [4]. For the autonomous air combat mode of UCAVs, the existing researches focused mainly on autonomous tactics and maneuver decision-making at a higher level of abstraction. A genetic fuzzy tree (GFT) based artificial intelligence (AI) that controls flights of the UCAV in air combat missions within an extreme-fidelity simulation was presented in [5], and the system was assessed as the most aggressive, responsive, dynamic and credible AI by retired Colonel [PERSON]. An intelligent air combat learning system inspired by the learning mechanisms of the brain was designed in [6], and experiments showed that the system can acquire confrontation abilities through self-learning without prior rules. For a one-on-one two-dimensional air combat maneuvering problem, [PERSON] et al. [7] proposed an approximate dynamic programming (ADP) approach for computing an efficient approximation of the optimal policy. A similar ADP method was used to analyze the influence of UCAV agility on the dogfight effectiveness in [8]. [PERSON] et al. [9] extended the above problem scenario to two-on-two engagements, and a novel autonomous air combat algorithm with high-performance and real-time calculations based on basic fighter maneuvers was proposed. There were also some other effective methods such as game theoretic approach [10; 11], Bayesian optimization [12; 13], and evolutionary algorithm [14] being applied to solve the air combat decision-making problem for UCAVs. The UCAV can obtain a favorable situational superiority in the \"shooter\" mode through the above solutions of air combat decision-making. To destroy the highly maneuvering aerial target effectively, the research on the guidance and control of the AAM after launch has become a hot spot. A robust adaptive guidance method for maneuvering targets was investigated in [15], which in the presence of impact angle constraint, input saturation, autopilot lag, and external disturbance. By robustly stabilizing a line-of-sight rate, [PERSON] et al. [16] proposed an integrated continuous finite-time disturbance observer/bounded continuous finite-time stabilizer strategy. Robust generalized dynamic inversion and sliding mode based neural network was used to design the guidance and control strategy for homing missiles in [17; 18], respectively. Furthermore, a novel cooperative mid-course guidance law was studied in [19] by combining the traditional trajectory shaping guidance law and the cooperative parameter.