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Multiple-model adaptive estimation using a residual correlationKalman filter bank

机译：使用残差相关的多模型自适应估计卡尔曼滤波器组

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摘要

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank within the MMAE. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique results in a delay in detecting the flight control actuator failure because several samples of the residual must be collected before the residual correlation can be estimated. However, it allows a significant reduction of the amplitude of the required system inputs for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers
机译：我们提出了一种改进的多模型自适应估计（MMAE）算法，该算法使用卡尔曼滤波器残差的时间相关性来代替其缩放幅度，从而为每个建模假设分配条件概率。这种经过修改的算法称为残差相关卡尔曼滤波器组（RCKFB），它使用残差相关性的估计值与常规MMAE假设测试算法的稍加修改版本一起使用，以将条件概率分配给以下各种假设：在MMAE中的Kalman滤波器组中建模。该概念用于检测飞行控制执行器故障，其中在MMAE内的基本滤波器的残差中存在单频正弦波（与时间高度相关），表明该滤波器具有错误的执行器故障状态假设。该技术导致检测飞行控制致动器故障的延迟，因为在可以估计残差相关性之前必须收集残差的几个样本。但是，它可以显着减小所需的系统输入的幅度，以激发各种系统模式以增强可识别性，使它们可能是潜意识的，从而不会引起飞行员和乘客的反对

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