Limited memory optimal filtering
Limited memory optimal filtering
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DOI：
10.1109/jacc.1968.4169105
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发表时间：
1968-10
影响因子：
6.8
通讯作者：
A. Jazwinski
中科院分区：
文献类型：
--
作者：
A. Jazwinski
Linear and nonlinear optimal filters with limited memory length are developed. The filter output is the conditional probability density function and, in the linear Gaussian case, is the conditional mean and covariance matrix where the conditioning is only on a fixed amount of most recent data. This is related to maximum-likelihood least-squares estimation. These filters have application in problems where standard filters diverge due to dynamical model errors. This is demonstrated via numerical simulations.
