Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in the reward function design. Through the analysis of the sign of the action space and design of the reward function in the adversarial form, the combat requirements can be satisfied in both the missile and target/defender training. Then, a curriculum-based deep reinforcement learning algorithm is applied to train the agents and a convergent game strategy is obtained. The simulation results show that the attack strategy of the missile can maneuver according to the battlefield situation and can successfully hit the target after avoiding the defender. The active defense strategy enables the less capable target/defender to achieve the effect similar to a network adversarial attack on the missile agent, shielding targets from attack against missiles with superior maneuverability on the battlefield.

A fixed-time nonlinear circular guidance law that satisfies the impact time constraint is proposed. By utilizing the geometric principle that the length of a circular arc connecting the missile and the target can be analytically calculated, the exact expression of time-to-go is obtained. Thus, the impact time error can be shaped to zero, and the missile can intercept the target at the desired time, which is crucial in a salvo attack. The settling time of the impact time error is proved to be bounded by a fixed time, which does not depend on initial conditions, but is only determined by two guidance parameters. Moreover, the criteria for choosing the guidance parameters values are established analytically, rather than by trial-and-error or empirically, which can provide valuable guidelines for guidance law designers. To address the look angle constraint, deviated pure pursuit (DPP) is employed, and switching logic between guidance laws is provided. Unlike many existing impact time control guidance laws, the formulation of the one proposed is based on nonlinear engagement kinematics, and the implementation does not execute numerical calculations, which can improve the guidance accuracy and reduce computation burdens on the guidance system. A series of nonlinear simulations are implemented to verify the effectiveness of the proposed guidance law.

An integrated navigation scheme based on multiple optical flow sensors and a strapdown inertial navigation system (SINS) are presented, instead of the global position system (GPS) aided. Multiple optical flow sensors are mounted on a micro air vehicle (MAV) at different positions with different viewing directions for detecting optical flow around the MAV. A fault-tolerant decentralized extended Kalman filter (EKF) is performed for estimating navigation errors by fusing the inertial and optical flow measurements, which can prevent the estimation divergence caused by the failure of the optical flow sensor. Then, the estimation of navigation error is inputted into the SINS settlement process for correcting the SINS measurements. The results verify that the navigation errors of SINS can be effectively reduced (even more than 9/10). Moreover, although the sensor is in a state of failure for 400 seconds, the fault-tolerant integrated navigation system can still work properly without divergence.