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Kalman filtering utilizing future dynamics for descriptor systems

机译:卡尔曼滤波利用未来动力学描述系统

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This paper studies the filtering problem of descriptor systems. The noncausal behaviour of descriptor systems leads to filtering that takes into account not only past and present dynamics, but also the future dynamics. Using the maximum likelihood estimation technique, a recursive filter for general time-varying descriptor systems is developed which makes use of past, present as well as one-step future dynamics. The existence condition of the filter is also given which is weaker than that of the filter in Nikoukhah et al. (1992) and is identical to the infinity observability in the time-invariant case.
机译:本文研究了描述符系统的过滤问题。描述符系统的非因果行为导致过滤不仅要考虑过去和现在的动态,而且还要考虑未来的动态。使用最大似然估计技术,开发了一种用于一般时变描述符系统的递归滤波器,该递归滤波器利用了过去,现在以及一步的未来动态。还给出了滤波器的存在条件,它比Nikoukhah等人的滤波器的存在条件要弱。 (1992年),并且在时不变情况下与无穷大可观测性相同。

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