arXiv subjects

Mingyan Gong

Publications and source records attributed to Mingyan Gong.

4 recordsLinked to original sources

arXiv2026

Deterministic Maximum Likelihood Direction Finding in the Mixture Noise of Gaussian and Spherically Invariant Components

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 deterministic maximum likelihood direction finding. In this context, the Expectation-Conditional Maximization (ECM) algorithm, an extension of the expectation-maximization algorithm, has been applied and designed. However, simulation results show that the ECM algorithm always improperly converges. In this article, the ECM Either (ECME) algorithm, an extension of the ECM algorithm, is applied and designed, which additionally utilizes the actual log-likelihood function to first update partial parameter estimates at every iteration and does not need to initialize all parameter estimates. Moreover, the deterministic Cramer-Rao low bounds (CRLBs) of DOA estimators are derived and compared. Simulation results indicate that the ECME algorithm exhibits proper convergence and its root mean square errors of DOA estimates asymptotically approach the CRLBs as the signal powers increase, i.e., the derived CRLBs are correct.

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arXiv2026

The AECM Algorithm for Deterministic Maximum Likelihood Direction Finding in the Presence of Gaussian Mixture Noise

Gaussian mixture noise can model non-Gaussian noise and also be used when outliers are present. For deterministic maximum likelihood direction finding in Gaussian mixture noise, the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, an extension of the expectation-maximization algorithm, was applied and designed by Kozick and Sadler twenty odd years ago, which simultaneously updates direction of arrival (DOA) estimates at each iteration and cannot properly converge under unequal signal powers. In this article, the Alternating Expectation-Conditional Maximization (AECM) algorithm, an extension of the SAGE algorithm, is applied and designed, which utilizes multiple less informative versions of the complete data and the golden section search method to update DOA estimates at each iteration sequentially (one by one). Theoretical analysis shows that the AECM algorithm has almost the same computational complexity of each iteration as the SAGE algorithm. However, numerical results show that the AECM algorithm yields faster stable convergence and is computationally more efficient.

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arXiv2025

The ECME Algorithm Using Factor Analysis for DOA Estimation in Nonuniform Noise

Factor analysis (FA) plays a critical role in psychometrics, econometrics, and statistics. Recently, maximum likelihood FA (MLFA) has been applied to direction of arrival (DOA) estimation in unknown nonuniform noise and a variety of iterative approaches have been developed. In particular, the Factor Analysis for Anisotropic Noise (FAAN) method proposed by Stoica and Babu has excellent convergence properties. In this article, the Expectation/Conditional Maximization Either (ECME) algorithm, an extension of the expectation-maximization algorithm, is designed again for MLFA by introducing new complete data, which can thus use two explicit formulas to sequentially update the estimates of parameters at each iteration and have excellent convergence properties. Theoretical analysis shows that the ECME algorithm has almost the same computational complexity at each iteration as the FAAN method. However, numerical results show that the ECME algorithm yields faster stable convergence and the convergence to the global optimum is easier. Importantly, MLFA is not the best choice for the subspace based DOA estimation in unknown nonuniform noise.

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arXiv2025

Angle Estimation of a Single Source with Massive Uniform Circular Arrays

Estimating the directions of arrival (DOAs) of incoming plane waves is an essential topic in array signal processing. Widely adopted uniform linear arrays can only provide estimates of source azimuth. Thus, uniform circular arrays (UCAs) are attractive in that they can provide $360^{\circ}$ azimuthal coverage and additional elevation angle information. Considering that with a massive UCA, its polar angles of array sensors can approximately represent azimuth angles over $360^{\circ}$ using angle quantization, a simple two-dimensional DOA estimation method for a single source is proposed. In this method, the quantized azimuth angle estimate is obtained by only calculating and comparing a number of covariances, based on which the elevation angle estimate is then obtained by an explicit formula. Thus, the proposed method is computationally simple and suitable for real-time signal processing. Numerical results verify that the proposed method can obtain azimuth as well as elevation angle estimates and the estimates can be used as starting points of multidimensional searches for methods with higher accuracy. Additionally, the proposed method can still work in the presence of nonuniform noise.

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