.png){kind=icon}
Return
Maximum Likelihood Estimation in Mixed Integer Linear Models
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2023.3324833.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We consider the maximum likelihood (ML) parameter estimation problem for mixed integer linear models with arbitrary noise covariance. This problem appears in applications such as single frequency estimation, phase contrast imaging, and direction of arrival (DoA) estimation. Parameter estimates are found by solving a closest lattice point problem, which requires a lattice basis. In this letter, we present a lattice basis construction for ML parameter estimation and conclude with simulated results from DoA estimation and phase contrast imaging.
Keywords:
Lattices
Maximum likelihood estimation
Maximum likelihood decoding
Direction-of-arrival estimation
Task analysis
Planar arrays
Noise measurement
Phase unwrapping
sphere decoding
lattices
Hermite normal form
Chinese remainder theorem
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSaveEconomics of One Health: Costs and benefits of integrated West Nile virus surveillance in Emilia-Romagna.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSave
View more