交互式多模型扩展卡尔曼滤波(IMM-EKF)算法是解决机动载体运动模型不确定的定位问题的次优算法,在载体做模型确定的运动时该方法仍得到次优解且浪费运算资源.针对IMM-EKF算法的此类缺陷,采用离线训练的概率神经网络模型,实时判断当前运动模型分类,在运动模型确定的状态下选择对应的单一模型进行运算,而在运动模型不确定的状态下选择IMM-EKF算法,既保证定位精度,又减少了不必要的运算量.仿真对比实验验证了相比于IMM-EKF算法,新算法在精度方面的优势.
Interacting multiple model extended Kalman filter(IMM-EKF) algorithm is a sub-optimal algorithm which can solve the positioning problem in which the motion model is uncertain.But this method still gets sub-optimal solution and wastes computational resources when the carrier does the motion of which the model is certain.Aiming at this kind of defects of IMM-EKF,the offline training probabilistic neural network model is adopted to judge the classification of current motion model in real time.We choose to operate with the single corresponding model when the motion model is in the state of certainty,and choose the IMM-EKF algorithm when the motion model is in the uncertain state.Thus it not only ensures the positioning accuracy,but also reduces the unnecessary computation burden.Simulation experiments verify the validity and accuracy of the algorithm,while the contrast test verifies the advantages in accuracy of the new algorithm compared with IMM-EKF algorithm.