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Deterministic Maximum Likelihood Direction Finding in the Mixture Noise of Gaussian and Spherically Invariant Components
Mingyan Gong
Spherically invariant (SI) random processes can model impulsive noise and unreliable measurements. Recently, the mixture noise of Gaussian and SI components has been used in determ…
The AECM Algorithm for Deterministic Maximum Likelihood Direction Finding in the Presence of Gaussian Mixture Noise
Mingyan Gong, Bin Lyu
Gaussian mixture noise can model non-Gaussian noise and also be used when outliers are present in measurements. For deterministic maximum likelihood direction finding in Gaussian m…
Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields
Ming-yan Gong, Bin Lyu
In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood dir…
EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise
Ming-yan Gong, Bin Lyu
The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonu…
EM and SAGE algorithms for DOA Estimation in the Presence of Unknown Uniform Noise
Ming-yan Gong, Bin Lyu
The expectation-maximization (EM) and space-alternating generalized EM (SAGE) algorithms have been applied to direction of arrival (DOA) estimation in known noise. In this work, th…