基于量测迭代更新集合卡尔曼滤波的机动目标跟踪算法

在机动目标跟踪中,用于模型辨识和状态估计的非线性滤波器的合理选择和优化是提升滤波精度的关键.融合量测迭代更新集合卡尔曼滤波和交互式多模型(interacting multiple models,IMM)方法,本文提出了基于量测迭代更新集合卡尔曼滤波的机动目标跟踪算法.通过迭代更新思想的引入构建了一种量测迭代更新下集合卡尔曼滤波的实现结构,并将其作为IMM的模型滤波器实现对于目标运动模式和状态的辨识与估计.针对算法结合过程中滤波精度和计算量的平衡,设计了用于输入交互环节的状态估计样本,同时简化输入交互环节和输出交互环节中滤波误差协方差矩阵的交互过程.理论分析和仿真结果验证了算法的可行性和有效性.

Maneuvering target tracking algorithm based on ensemble Kalman filter with observation iterated update

The reasonable selection and optimization of nonlinear filter used in maneuvering target tracking is the key to realize the identification model and the estimation of state. Combining with ensemble Kalman filter with observation iterated update and interacting multiple models, a novel maneuvering target tracking algorithm based on ensemble Kalman filter with observation iterated update is proposed. Firstly, according to the mechanism of iterated observation update, the realization framework of ensemble Kalman filter with observation iterated update is constructed. And then, the improved method is taken as model filter in interacting multiple models to identify and estimate the motion mode and target state. Aiming to the balance between filtering precision and calculated amount in the combination of two algorithms, state estimation samples are designed to use in interactive input, and simultaneously the interactive processes of estimation error covariance matrix in interactive input and interactive output are simplified. The theoretical analysis and experimental results show the feasibility and validity of the new algorithm.