一种马尔可夫矩阵自适应的IMM-CKF算法
发布时间：2019-01-25 08:56
【摘要】：为了解决标准的交互式多模型(Interacting Multiple Model,IMM)算法中Markov概率转移矩阵固定不变的问题,结合容积卡尔曼滤波(Cubature Kalman Filter,CKF)算法,提出了一种Markov概率转移矩阵自适应的IMM-CKF算法。该算法引入了一个Markov矩阵元素的调整系数,在滤波过程中自适应调整Markov概率转移矩阵的每一个元素。新算法大幅度提高了匹配模型的概率,降低了非匹配模型的影响,同时改善了标准IMM算法的滤波效果。最后,通过蒙特卡洛仿真实验验证了自适应IMM-CKF算法的跟踪效果比IMM-CKF算法更好。
[Abstract]:In order to solve the problem of fixed Markov probability transfer matrix in the standard interactive multi-model (Interacting Multiple Model,IMM algorithm, the volumetric Kalman filter (Cubature Kalman Filter,CKF) algorithm is used to solve the problem. An adaptive IMM-CKF algorithm for Markov probabilistic transition matrix is proposed. The algorithm introduces the adjustment coefficient of a Markov matrix element and adaptively adjusts every element of the Markov probability transfer matrix in the filtering process. The new algorithm greatly improves the probability of the matching model, reduces the influence of the mismatch model, and improves the filtering effect of the standard IMM algorithm. Finally, the Monte Carlo simulation results show that the adaptive IMM-CKF algorithm has better tracking performance than the IMM-CKF algorithm.
【作者单位】： 空军预警学院;
【基金】：学院科研创新基金重大基础研究专项课题(No.2014ZDJC0102)
【分类号】：TN713
，
本文编号：2414988
[Abstract]:In order to solve the problem of fixed Markov probability transfer matrix in the standard interactive multi-model (Interacting Multiple Model,IMM algorithm, the volumetric Kalman filter (Cubature Kalman Filter,CKF) algorithm is used to solve the problem. An adaptive IMM-CKF algorithm for Markov probabilistic transition matrix is proposed. The algorithm introduces the adjustment coefficient of a Markov matrix element and adaptively adjusts every element of the Markov probability transfer matrix in the filtering process. The new algorithm greatly improves the probability of the matching model, reduces the influence of the mismatch model, and improves the filtering effect of the standard IMM algorithm. Finally, the Monte Carlo simulation results show that the adaptive IMM-CKF algorithm has better tracking performance than the IMM-CKF algorithm.
【作者单位】： 空军预警学院;
【基金】：学院科研创新基金重大基础研究专项课题(No.2014ZDJC0102)
【分类号】：TN713
，
本文编号：2414988
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