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首页> 外文期刊>IEEE Transactions on Automatic Control >Accurate State Estimation in Continuous-Discrete Stochastic State-Space Systems With Nonlinear or Nondifferentiable Observations
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Accurate State Estimation in Continuous-Discrete Stochastic State-Space Systems With Nonlinear or Nondifferentiable Observations

机译：具有非线性或不可微观测的连续离散随机状态空间系统中的精确状态估计

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摘要

This paper presents a novel method of nonlinear Kalman filtering, which unifies the best features of the accurate continuous-discrete extended and cubature Kalman filters. More precisely, the time updates in the discussed state estimator are done as those in the first filter whereas the measurement updates are conducted with use of the third-degree spherical-radial cubature rule applied for approximating the arisen Gaussian-weighted integrals. All this allows accurate predictions of the state mean and covariance matrix to be combined with accurate measurement updates. Moreover, the new filter is particularly effective for continuous-discrete stochastic systems with nonlinear and/or nondifferentiable observations. The efficiency of this mixed-type method is shown in comparison to the performance of the original accurate continuous-discrete extended and cubature Kalman filters in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, with sufficiently long sampling times.
机译：本文提出了一种新的非线性卡尔曼滤波方法，该方法统一了精确的连续离散扩展和容积卡尔曼滤波器的最佳特征。更准确地说，所讨论的状态估计器中的时间更新与第一个滤波器中的时间更新相同，而测量更新是通过使用三次球面辐射量法则规则进行的，该规则用于近似所产生的高斯加权积分。所有这些使状态均值和协方差矩阵的准确预测与准确的测量更新结合在一起。此外，新滤波器对于具有非线性和/或不可微观测的连续离散随机系统特别有效。与原始精确连续离散扩展和库尔曼卡尔曼滤波器在恶劣条件下解决七维雷达跟踪问题的性能相比，该混合方法的效率得到了证明，在这种情况下飞机执行协调的转弯，并且采样时间长。

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