具有噪声信息与状态模型不确定系统的IMM自适应滤波

卡尔曼滤波器广泛用于解决线性高斯系统的状态估计问题.然而,在实际应用中过程噪声和系统模型参数先验信息未知,且量测受到异常值干扰,给准确估计系统状态带来极大困难.针对具有噪声信息和状态模型不确定的动态系统,提出一种广义交互式多模型自适应滤波算法.该算法设计多个模型并行的方式对系统不确定进行处理,对于每个模型,建立Skew-T分布非对称重尾噪声表示模型,为了解决过程噪声与系统协方差相互耦合难以求解的问题,利用逆威沙特分布对系统预测协方差矩阵进行描述,并通过变分贝叶斯推理递归计算系统状态的后验分布.仿真结果和实验验证表明,在噪声信息和状态模型不确定条件下,所提出算法具有较高的估计精度.

Interactive multiple model adaptive filter for system with uncertain state model and noise information

The Kalman filter is widely used to solve the state estimation problem of linear Gaussian systems. Due to the unknown prior knowledge of the process noise and state model, and outliers in measurement, accuracy state estimation is difficult. In this paper, a general interactive multiple model adaptive filter is proposed to estimate the state of the system with uncertain state model and noise information. The algorithm designs a bank of filters in parallel to deal with the system model uncertainy. In each filter, the Skew-T distribution is used to model asymmetric Heavy-tailed measurement noise. To deal with the problem the process noise and system parameter are coupled, the covariance matrix of the system is assumed as inverse Wishart distributed. Then, the posterior distribution of the system state are joint recursively calculated by variational inference. The simulation and experimental results demonstrate that the proposed algorithm has better estimation accuracy with uncertain system models and noise information.