An introduction to factor graphs
An introduction to factor graphs
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DOI：
10.1109/msp.2004.1267047
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发表时间：
2004-01-01
影响因子：
14.9
通讯作者：
Loeliger, HA
中科院分区：
文献类型：
--
作者：
Loeliger, HA
图形模型，如因子图，允许一个统一的方法，在编码和信号处理，如迭代解码的Turbo码，LDPC码和类似的代码，联合解码，均衡，参数估计，隐马尔可夫模型，卡尔曼滤波和递归最小二乘法的一些关键问题。图形模型可以表示复杂的现实世界的系统，这样的表示有助于在广泛的应用领域中获得实用的检测/估计算法。大多数已知的信号处理技术-包括梯度方法、卡尔曼滤波和粒子方法-可以用作此类算法的组件。除了大多数以前的文献，我们使用Forney风格的因子图，它支持层次建模，并与标准框图兼容。
Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams.