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Characterization of Kalman filter residuals in the presence ofmismodeling

机译：存在模型错误的卡尔曼滤波器残差的表征

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摘要

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices
机译：针对卡尔曼滤波器模型与精确表示真实系统（真模型）的线性模型不同的特定情况，计算卡尔曼滤波器残差的均值和协方差。多模型自适应估计（MMAE）使用一组具有不同内部模型的卡尔曼滤波器，以及一种假设检验算法，该算法使用该组卡尔曼滤波器的残差来估计真实的系统模型。最多只有一个卡尔曼滤波器模型会与真模型完全匹配，并会产生一个均值和标准差已经分析过的残差。所有其他过滤器都使用内部模型，它们对真实系统进行了错误建模。我们计算了错误建模的输入矩阵，输出矩阵和状态转换矩阵对这些残差的影响。将计算出的均值和协方差与对应错误模型的输入矩阵和输出矩阵的飞行控制故障的仿真结果进行比较

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