Optimal joint detection and estimation in linear models
Optimal joint detection and estimation in linear models
复制标题
线性模型中的最优联合检测和估计
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
H. Poor
中科院分区：
文献类型：
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作者：
Jianshu Chen;Yue Zhao;A. Goldsmith;H. Poor
研究了带高斯噪声的线性模型的最优联合检测与估计问题。导出了（多个）假设和状态的联合后验分布的一个简单的封闭表达式。该表达式明确了最优检测器对状态估计的依赖性。联合后验分布表征了关于假设和状态值的信念（“软信息”）。此外，它是联合检测多个假设和估计状态的充分统计量。所开发的表达式为所有性能标准下的联合检测和估计提供了一个统一的框架。
The problem of optimal joint detection and estimation in linear models with Gaussian noise is studied. A simple closed-form expression for the joint posterior distribution of the (multiple) hypotheses and the states is derived. The expression crystalizes the dependence of the optimal detector on the state estimates. The joint posterior distribution characterizes the beliefs (“soft information”) about the hypotheses and the values of the states. Furthermore, it is a sufficient statistic for jointly detecting multiple hypotheses and estimating the states. The developed expressions give us a unified framework for joint detection and estimation under all performance criteria.