A Novel Angular Estimation Method in the Presence of Nonuniform Noise

Esfandiari, Majdoddin; Vorobyov, Sergiy A.

doi:10.1109/icassp43922.2022.9747777

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…In Fig. 2, we compare the ECME algorithm with two subspace based algorithms, which utilize the state-of-the-art subspace separation approaches in [17]- [18] and are called "Approach 1+Root-MUSIC" and "Approach 2+Root-MUSIC", respectively. The three algorithms process the same snapshots for each trial.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We utilize the mathematical induction method. If Q (u) ≻ 0 W (or δ (u) > 0) and O (u) 0 V , we have (14) and then in (18) 13). The proof is completed.…”

Section: B Stability and Complexitymentioning

confidence: 99%

“…In nonuniform noise, the covariance matrix still keeps a diagonal structure but the diagonal elements are no longer identical, which makes direction of arrival (DOA) estimation difficult. To tackle the problem of direction finding in unknown nonuniform noise, diverse subspace separation approaches based on the subspace technique have been proposed in the literature [13]- [18].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

Gong¹,

Liu²

2023

Preprint

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In this letter, we employ and design the expectationconditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm the effectiveness of the algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

“…We utilize the mathematical induction method. If Q (u) ≻ 0 W (or δ (u) > 0) and O (u) 0 V , we have (14) and then in (18) 13). The proof is completed.…”

Section: B Stability and Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

Gong¹,

Liu²

2023

Preprint

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“…Moreover, some statistical properties of both the deterministic and random ML based DOA estimators under unknown uniform noise are compared in [ 12 ]. In addition to uniform noise, non-uniform noise has also attracted increasing attention [ 13 , 14 , 15 , 16 ]. The non-uniform noise has an arbitrary diagonal covariance matrix and, thus, makes DOA estimation complex.…”

Section: Introductionmentioning

confidence: 99%

EM and SAGE Algorithms for DOA Estimation in the Presence of Unknown Uniform Noise

Gong

Lyu

2023

Sensors

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The existing expectation maximization (EM) and space-alternating generalized EM (SAGE) algorithms are only applied to direction of arrival (DOA) estimation in known noise. In this paper, the two algorithms are designed for DOA estimation in unknown uniform noise. Both the deterministic and random signal models are considered. In addition, a new modified EM (MEM) algorithm applicable to the noise assumption is also proposed. Next, these EM-type algorithms are improved to ensure the stability when the powers of sources are not equal. After being improved, simulation results illustrate that the EM algorithm has similar convergence with the MEM algorithm, the SAGE algorithm outperforms the EM and MEM algorithms for the deterministic signal model, and the SAGE algorithm cannot always outperform the EM and MEM algorithms for the random signal model. Furthermore, simulation results show that processing the same snapshots from the random signal model, the SAGE algorithm for the deterministic signal model can require the fewest computations.

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“…These two methods are first applied under uniform Gaussian noise, which decreases the number of parameters and simplifies the problem. However, the uniform noise model is unrealistic in many situations, and nonuniform noise has been considered in numerous papers [4,[13][14][15][16][17][18][19]. In nonuniform noise, the covariance matrix still keeps a diagonal structure, but the diagonal elements are no longer identical, which hinders direction-of-arrival (DOA) estimation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

Gong

Lyu

2023

Electronics

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The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of the expectation–maximization algorithm, for solving the ML direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas, and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm that the ECME algorithm can efficiently obtain the ML based DOA estimate of each stochastic source.

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A Novel Angular Estimation Method in the Presence of Nonuniform Noise

Cited by 7 publications

References 23 publications

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

EM and SAGE Algorithms for DOA Estimation in the Presence of Unknown Uniform Noise

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

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