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由于组合导航系统具有强非线性和模型不确定性的特点,工程中扩展卡尔曼滤波无法满足组合导航系统实际应用的要求.为此,针对贝叶斯框架下高斯类非线性滤波算法的估计性能给出具体分析.首先,在估计点处对非线性函数进行泰勒展开获得泰勒近似,通过一阶矩和二阶矩分析滤波算法的近似精度;然后,通过数值稳定性对非线性滤波算法进行分析;最后,分别采用低维和高维模型对各滤波算法进行对比分析,为组合导航系统的实践提供借鉴.
Due to the strong nonlinearity of the integrated navigation system and the uncertainty of the model, the extended Kalman filter can not meet the requirements of the integrated navigation system.Therefore, the performance of Gaussian nonlinear filtering algorithm in Bayesian framework A detailed analysis is given.Firstly, the Taylor approximation of the nonlinear function is carried out at the estimation point, and the approximate accuracy of the filtering algorithm is analyzed by the first moment and the second moment. Then, the nonlinear filtering algorithm is analyzed by numerical stability Finally, the low-dimensional and high-dimensional models are used respectively to compare and analyze the filtering algorithms to provide reference for the practice of integrated navigation system.