From Wiener to hidden Markov models
From Wiener to hidden Markov models
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DOI：
10.1109/37.768539
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发表时间：
1999-06-01
影响因子：
5.7
通讯作者：
Anderson, BDO
中科院分区：
文献类型：
--
作者：
Anderson, BDO
作者研究了3种类型的滤波器的共同性质，通过考虑各种随机模型：维纳滤波器，卡尔曼滤波器和隐马尔可夫模型（HMM）滤波器。特别突出的统一功能是忘记旧数据和初始条件，并防止舍入误差影响计算。它们区分了固定滞后平滑和滤波的概念，并揭示了它们的相对优点和缺点。再一次，有一些共同的属性允许统一的观点。我们特别关注最有效的平滑滞后的特征，并确定的SNR的情况下，平滑是特别有益的。其动机是处理来自潜艇拖曳的声学传感器阵列的数据。
The authors investigate common properties of 3 types of filters obtained by considering various stochastic models; Wiener filters, Kalman filters and hidden Markov model (HMM) filters. Unifying features which particularly stand out are the forgetting of old data and of initial conditions, and protection from round-off error effects' overpowering the calculations. They differentiate the concept of fixed-lag smoothing from filtering, and expose the comparative advantages and disadvantages. Once again, there are common properties which allow a unified viewpoint. We focus especially on characterizations of a maximally effective smoothing lag, and identification of the SNR circumstances under which smoothing is especially beneficial. The motivation is the processing of data from an array of acoustic sensors towed by a submarine.