.png){kind=icon}
返回
Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2015.2498195.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter addresses a maximum likelihood (ML) algorithm for multidimensional (m-D) harmonic retrieval (MHR) problems. The new algorithm iteratively estimates the parameters in a rough to fine manner, intervened with filtering processes to separate the signals into appropriate groups. To facilitate implementations of the ML estimation, a Monte Carlo method, importance sampling (IS), and the theorem of Pincus are utilized to determine the ML estimates. Moreover, the pairing of the estimated parameters is automatically achieved without extra overhead. Conducted simulations demonstrate that the new algorithm outperforms the main state-of-the-art works and can achieve the Cramer-Rao lower bound (CRLB) even in low signal-to-noise ratio (SNR) scenarios.
Keyword:
Filtering
importance sampling
maximum likelihood
multidimensional harmonic retrieval
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Hydraulic conditions, oxygenation, temperatures and sediment relationships of bedwork watermeadows
.png){kind=icon}
PREAI
分享收藏Relation Between Blood Resistivity and Hematocrit in Fresh Human Fetal Blood新鲜人胎儿血液中血液电阻率和血细胞比容的关系
OAAI
OAAI分享收藏The Role and Impact of Biophysical Determinants on Present and Future Land Use Patterns in Europe
PREAI
分享收藏Vibration Control and Failure Diagnosis in Rotating Machinery by means of Active Magnetic Bearings
PREAI
分享收藏
查看更多内容