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针对非线性系统模型参数未知情况下的状态估计问题,提出一种融合极大后验估计的交互式容积卡尔曼滤波算法(InCKF).该算法利用二阶斯特林插值公式和无迹变换对非线性函数的近似思想,实现对模型未知参数的确定,从而使滤波算法摆脱对模型参数精确已知的依赖,并通过容积卡尔曼滤波算法完成状态估计和量测更新.仿真结果表明,相比于经典的参数扩维方法,InCKF算法具有更高的精度和更强的数值稳定性.
In order to solve the problem of state estimation under the condition that the parameters of nonlinear system are unknown, an interactive volumetric Kalman filter algorithm (InCKF) based on max-posteriori estimation is proposed. The algorithm uses the second-order Stirling interpolation formula and the unscented transform Nonlinear function to realize the determination of the unknown parameters of the model, so that the filtering algorithm can get rid of the exact known dependence on the model parameters and complete the state estimation and measurement updating through the volumetric Kalman filtering algorithm. The simulation results show that, In classical parameter expansion method, InCKF algorithm has higher accuracy and stronger numerical stability.