5/6/2023 0 Comments 2v2 air combat maneuvers![]() To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. We also advocate referring to the approaches/techniques that are utilized in other similar fields in devising the Autonomous Air Combat solutions. Via this paper, we hope to deliver an in-depth analysis of past experiences and potential challenges/solutions for the Autonomous Air Combat technique. traditional approaches enhanced by the novel data-driven technique. Inspired by the state-of-art techniques in other similar fields (robotics, autonomous driving), we also propose potential solutions, i.e. ![]() to abstract and emulate the human pilot experiences, and to develop the online learning capabilites. We point out certain technical paths/challenges that need to be addressed in the future Autonomous Air Combat development, i.e. We also comment on both weakness and strengths for each group/method. In each group, we present the representative methods first problem definition, solution, and a brief overview of the historical development are illustrated. mathematics-based, knowledge-encoded, and learning-driven. We divide different Autonomous Air Combat solutions into three groups, i.e. Based on our survey, a review of own aircraft guidance/control in the (primarily one-to-one) Autonomous Air Combat solutions is presented. While the perception in the first fold serves as a foundation, this paper is mainly focused on the second one. In devising the Autonomous Air Combat solutions, we follow similar methodologies in the robotics community, and divide the overall scheme into two folds: the perception of other (enemy/friendly) aircraft, and the guidance/control for own aircraft. However, no complete solutions seem to have appeared because of the highly dynamic and complex nature of the Autonomous Air Combat problem. The Autonomous Air Combat technique has been a lasting research topic for decades.
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