Abstract:

For the sake of changing main conditions required by multi-targets attack optimization and establishing the new direction of the decisionmaking optimization during attacking action executing by the multiple unmanned combat aerial vehicle (UCAV), a dynamic awareness model for threats based on a kind of graphical model， dynamic Bayesian network (DBN)， is constructed. Based on this graphical model， a novel layered conceptual framework for autonomous optimization systems and an autonomous cooperative planning method for UCAVs are advanced. With parameters derived from the fusion data and observing their change in relative DBN’s transition networks in virtue of DBN’s learning and inference algorithms， this method realizes the online awareness for complicated aerospace surroundings. In the light of this dynamic awareness model， attack targets re-assignment and cooperative path re-planning for UCAV are achieved in accordance with an identified guideline. Simulation results demonstrate that this autonomous planning method is proper and valid.