3/15/2023 0 Comments Ffxiv air combat maneuversIn velocity generation part, a PID based method and a fuzzy inference based method are employed to compute the reference velocity of the pursuer and the results obtained with these two approaches are compared. In navigation part the reference values for bank, heading and flight path angles are computed. The attack algorithm consists of navigation and reference velocity calculation steps. In this paper a novel attack algorithm is introduced for UCAVs, with the assumption that in order to launch an effective attack the pursuer aircraft must be able to follow the target aircraft from behind with a predetermined distance. There are challanges for the design of this kind of an algorithm such as real-time operability and the case with inadequate number of sensors on the UAVs. In order to use the UCAVs in the air to air combat, required algorithms must be carefully designed to enhance them with aerial combat competence. Unmanned combat air vehicles (UCAV) are finding increasing use in military applications due to their autonomy, processing capabilities and low cost with respect to manned flights. The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results. In the simulation of air combat, three initial scenarios are set, namely, opposite, offensive and defensive conditions. Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO. Besides, the convergence and time complexity of TLPIO are discussed. Significantly, the proposed TLPIO does not initialize the population randomly, but adopts the transfer learning method based on Kullback-Leibler (KL) divergence to initialize the population, which improves the search accuracy of the optimization algorithm. Finally, a key point is that the objective function to be optimized is designed using the game mixed strategy, and the optimal mixed strategy is obtained by TLPIO. Then, the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats. Secondly, a 3-degrees-of-freedom (3-DOF) aircraft model is used as a maneuvering command generator, and the expanded elemental maneuver library is designed, so that the aircraft state reachable set can be obtained. Firstly, a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform. This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization (TLPIO) for unmanned combat aerial vehicles (UCAVs) in dogfight engagements. The simulation results verify that all three previously proposed methods can accurately predict the next move based on historical data. And we propose a filter to improve the accuracy of the online prediction method for trajectory prediction with noise. Furthermore, we propose an online prediction method, and it can accurately predict the subsequent trajectory of flying vehicles when the type of trajectory is not clear. Based on the results of classification using the offline LSTM, we propose two trajectory prediction methods, and both of them achieve excellent prediction with and without noise interference. Accordingly, we train the offline Long-Short Term Memory (LSTM) using 1/12 of the dataset of trajectories, and results show that the trained network can classify types of trajectories accurately. Next, the data set for neural network training is created by varying the critical parameters of the trajectory equation. Then, according to the kinematics model of the flying vehicle, eight kinds of typical maneuver trajectory equations are introduced. In this paper, we investigate eight typical types of maneuvers of flying vehicles. To deal with the threat from flying vehicles, it is of great significance to accurately predict the flight trajectory of flying vehicles using the known historical position data.
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