基于复合导引头态势的抗干扰评估方法OA

To solve the problems of difficulty in obtaining evaluation information and strong subjectivity of anti-jamming party in the evaluation of radar anti-jamming effects,the anti-jamming evaluation index system is established based on the situation change of the active and passive composite seeker as the main line,the position situation perceived by the active seeker and the situation of the seeker itself,as well as the situation information detected by the active/passive seeker from the enemy's active jamming signals.The anti-jamming performance of the seeker is evaluated by using the Sparrow Search Algorithm-Convolutional Neural Network(SSA-CNN)method.When the situation information is missing,the Convolutional Auto-Encoder(CAE)is used to evaluate the anti-jamming effect of the seeker with incomplete information.The simulation results show that two AI algorithms have excellent effectiveness and accuracy in evaluating the anti-jamming performance of the seeker in the complex jamming environment.