Computational requirements for a discrete Kalman filter
Computational requirements for a discrete Kalman filter
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DOI：
10.1109/tac.1971.1099837
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发表时间：
1971-12
影响因子：
6.8
通讯作者：
J. Mendel
中科院分区：
文献类型：
--
作者：
J. Mendel
卡尔曼滤波有多实用？这个问题的一个答案是由过滤器的计算要求提供的。计算要求-每个周期的计算时间(迭代)和所需的存储-决定了最小采样率和计算机内存大小。在本文中，这些要求是离散卡尔曼滤波重要系统矩阵的维度的函数。讨论了两种类型的测量处理：同步测量处理和顺序测量处理。结果表明，在多个批次中处理统计上独立的测量数据，然后使用顺序处理，通常比通过同时处理将它们一起处理要好。
How practical is a Kalman filter? One answer to this question is provided by the computational requirements for the filter. Computational requirements-computational time per cycle (iteration) and required storage-determine minimum sampling rates and computer memory size. These requirements are provided in this paper as functions of the dimensions of the important system matrices for a discrete Kalman filter. Two types of measurement processing are discussed: simultaneous and sequential. It is shown that it is often better to process statistically independent measurements in more than one batch and then to use sequential processing than to process them together via simultaneous processing.