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2-D Unconditional Maximum Likelihood DOA Estimation Based on Majorization-Minimization
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AI
DOI：10.1109/TVT.2024.3467394.png)
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摘要
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In this paper, we propose a majorization-minimization (MM) based refinement strategy tailored for two-dimensional (2-D) unconditional maximum likelihood (UML) direction-of-arrival (DOA) estimation of a single source. We introduce two surrogate functions (linear and quadratic) for 2-D UML DOA estimation with the aim of successively reducing the objective function's value. The proposed MM method guarantees convergence to the objective function's stationary point. Furthermore, we employ the backtracking squared iterative method (SQUAREM) to accelerate the convergence speed of the proposed MM method. Numerical experiments further validate the efficiency of our proposed MM method.
Keyword:
Direction-of-arrival estimation
Unified modeling language
Vectors
Convergence
Maximum likelihood estimation
Linear programming
Minimization
Array signal processing
direction-of-arrival (DOA) estimation
majorization-minimization (MM)
unconditional maximum likelihood (UML)
optimization
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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