RIA: Self-Adaptive Maximum-Likeihood Sequence Estimation

RIA：自适应最大似然序列估计

• 批准号: 9309044
• 负责人: Bernd-Peter Paris
• 金额: $ 9.73万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-07-15 至 1996-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9309044&HistoricalAwards=false
• 关键词: RIA; Self; Adaptive; Maximum; Likeihood

9309044 Paris The Principal Investigator is addressing the estimation of the most likely state sequence of a discrete-time finite-state Markov process with unknown parameters observed in independent noise. A maximum likelihood criterion over both the input sequence and the parameters is introduced for estimating the state sequence without using an embedded training sequence. Asymptotically, this estimator is expected to be close to the maximum-likelihood sequence estimator with completely known parameters. To facilitate the search for the most likely state sequence, the investigator is introducing computationally simple algorithms which are guaranteed to converge. Besides developing the underlying theory and algorithms, the research is focussing on applications to digital mobile radio communication systems. The award provides support for three years.

9309044 Paris 首席研究员正在解决在独立噪声中观察到的未知参数的离散时间有限状态马尔可夫过程的最可能状态序列的估计。 引入输入序列和参数的最大似然准则来估计状态序列，而不使用嵌入的训练序列。 渐进地，该估计器有望接近具有完全已知参数的最大似然序列估计器。 为了便于搜索最可能的状态序列，研究人员引入了计算简单的算法，保证收敛。 除了开发基础理论和算法外，该研究还重点关注数字移动无线电通信系统的应用。 该奖项提供三年的支持。