改进的自适应扩展卡尔曼滤波雷达目标跟踪算法OA北大核心CSTPCD

Kalman filter(KF)algorithm is the most commonly used algorithm in radar target tracking.However the tracking accuracy of KF filtering algorithm decreases when the adaptation of nonlinear motion model and the noise model mismatches.According to these problems,an improved adaptive extended kalman filter(EKF)algorithm for radar target tracking is proposed in the maneuvering target scene,the predicted position information is corrected through the deviation range of the target position.And then the back-propagation(BP)neural network algorithm is used to adapt to the correction of the predicted information results with the EKF algorithm.According to the noise impact of the actual situation,the adjusted update factor is used for the weight processing of the corrected EKF prediction position information,the measured information and the corrected BP-EKF prediction information value.The optimal location prediction information is adaptively selected based on the optimization model.The simulation results show that the filtering accuracy and stability are improved in target tracking.