Improved Adaptive IMM-UKF Algorithm Based on Monotonous Transformation of Model Probability
Considering the hysteresis of model switching and the slow conversion rate of existing adaptive interacting multiple models,an improved algorithm of adaptive interacting multiple models with an unscented Kalman filter based on monotone transformation of model probability(mIMM-UKF)is proposed.In this algorithm,the monotonicity of the model probability in the posterior information is used,and this algorithm makes a secondary modification to the Markov probability transition matrix and model estimation probability is introduced.Consequently,an accelerated switching speed and conversion rate of the matching model are obtained.The simulation results show that compared to existing algorithms,this algorithm significantly improves the accuracy of target tracking by enabling swift switching of matching models.
万方数据
维普
