Maximum Likelihood Estimation in Mixed Integer Linear Models

被引:0
作者
Tucker, David [1 ]
Zhao, Shen [2 ]
Potter, Lee C. [1 ]
机构
[1] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
[2] Stanford Univ, Dept Med, Div Cardiovasc Med, Stanford, CA 94304 USA
关键词
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; PHASE; REDUCTION; PERFORMANCE; ALGORITHMS; FREQUENCY; AMBIGUITY; SEARCH; DESIGN;
D O I
10.1109/LSP.2023.3324833
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:1557 / 1561
页数:5
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