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A receding horizon Kalman FIR filter for discrete time-invariant systems

机译:用于离散时不变系统的后视地平线Kalman FIR滤波器

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A receding horizon Kalman FIR filter is presented that combines the Kalman filter and the receding horizon strategy when the horizon initial state is assumed to be unknown. The suggested filter is a FIR filter form which has many good inherent properties. It can always be defined irrespective of singularity problems caused by unknown information about the horizon initial state. The suggested filter can be represented in either an iterative form or a standard FIR form. It is also shown that the suggested filter possesses the unbiasedness property and the remarkable deadbeat property irrespective of any horizon initial condition. The validity of the suggested filter is illustrated by numerical examples.
机译:提出了一种后视地平线卡尔曼FIR滤波器,当假定地平线初始状态未知时,该组合卡尔曼滤波器和后视地平线策略。建议的滤波器是FIR滤波器形式,具有许多良好的固有属性。无论是否有关于地平线初始状态的未知信息引起的奇异性问题,都可以始终定义它。建议的滤波器可以以迭代形式或标准FIR形式表示。还表明,无论水平起始条件如何,建议的滤波器都具有无偏性和显着的无差拍性。数值示例说明了建议的过滤器的有效性。

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