基于AIMM-SRCKF的机动目标跟踪算法

为了解决交互式多模型(interacting multiple model, IMM)算法在目标机动时模型切换速度迟缓的问题,给出了一种Markov概率转移矩阵在线修正的自适应IMM(adaptive IMM, AIMM)算法,利用IMM子模型中连续两个时刻之间的概率差来调整Markov概率转移矩阵,以提高子模型的切换速度和分配的合理性,同时提高了IMM算法的跟踪精度。其次,将平方根容积卡尔曼滤波(square-root cubature Kalman filter, SRCKF)引入到AIMM算法中,以解决在迭代滤波过程中协方差矩阵出现的非正定的问题,改善了数值稳定性,提出一种适用于机动目标跟踪的AIMM-SRCKF算法,仿真实验结果表明,该算法能提高匹配模型概率,缩短模型切换时间。

Maneuvering target tracking algorithm based on AIMM SRCKF

To solve the problem of model switching slowness in interactive multiple model(IMM) algorithm when the target is maneuvering, an adaptive IMM(AIMM) algorithm with Markov probability transfer matrix online correction is presented. The Markov probability transfer matrix is adjusted by the probability difference between two consecutive moments in the IMM sub-model to improve the switching speed and the rationality of the assignment of the sub-model, and the tracking accuracy is improved. Secondly, square-root volumetric Kalman filter(SRCKF) is introduced into AIMM algorithm to solve the non-positive definite problem of covariance matrix and improve the numerical stability during iterative filtering. AIMM-SRCKF algorithm for maneuvering target tracking is proposed. The simulation results show that the algorithm can improve the probability of matching models and shorten the model switching time.