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. The simulations demonstrated that the proposed scheme has considered the combat constraints and results of generated signals are compared with real air combat scenarios. Two BFM maneuvers are designed-one for offensive and one for defensive-to test the behavior of the system. Depending on the relative geometry of both sides, air combat controller decides on the movement and synthesizes the necessary control signal. The solution considers the energy conversion and turn radius heuristics. In this paper, a solution is proposed that chooses the right movement to take advantage on the other aircraft. Long term trajectory planning is not possible since relative geometry changes instantaneous. Control objective is getting to an advantageous position rather than following a constant trajectory. In air combat there are more than one aircraft and relative geometry of both sides are also included into this complex problem. Autonomous Control of UAV is a complex problem that has many parameters requiring low level robust control.
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