Deep Reinforcement Learning Guidance Method Considering the Field-of-view Angle Constraint of Passive Detection
A deep reinforcement learning guidance method is proposed to address the problem of guidance law design for intercepting maneuverable targets with infrared-guided missiles,taking into consideration pure angle measurements and field-of-view angle constraints.Firstly,the interception guidance problem is formulated as a Markov Decision Process.A deep reinforcement learning guidance model is established based on the double delay deep deterministic policy gradient(TD3)algorithm,giving thorough consideration to the first-order autopilot characteristics of the missile.Secondly,a comprehensive reward function is designed to consider the field-of-view angle constraints of the passive seeker while balancing energy consumption and guidance accuracy,and the guidance law of deep reinforcement learning is trained in a variety of typical scenarios.The comparison simulation and Monte Carlo simulation are carried out under the condition of different maneuvering modes of the target.The simulation results show that through the method,the mssile can hit the target with high accuracy under the premise of meeting the constraint of the field-of-view angle and the constraint of overload instruction saturation by using the pure angle information detected by the infrared seeker.Meanwhile,it has strong robustness to different maneuvering modes of the target.
Deep reinforcement learningManeuvering targetField-of-view angle constraintPure angular measurementInfrared guidanceMissile interception
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