Sparse array DOA estimation in the presence of unknown non-uniform noise

Goncharova, Anna A.; Weber, Raymond J.; Huang, Yikun

doi:10.1109/aero.2011.5747314

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Existing EM type algorithms for stochastic ML direction finding are only applicable to uncorrelated sources [9], [12], [20], i.e., O is diagonal. In this section, we employ the ECME algorithm [21], a generalisation of the EM algorithm, to solve problem (3) associated with correlated sources.…”

Section: Ecme Algorithmmentioning

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: Ecme Algorithmmentioning

confidence: 99%

“…In order to reduce the computational complexity, two classic methods have been developed: alternating maximization (AM) [8] and expectation-maximization (EM) [9]- [12] type algorithms. Early, these two methods are applied under uniform Gaussian noise, which decreases the number of parameters and simplifies the problem.…”

Section: Introductionmentioning

confidence: 99%

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

Gong¹,

Liu²

2023

Preprint

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“…In Compressive sensing we try to solve an underdetermined system [32]. Such that any sparse representation of the signal can be reconstructed from a small number of samples.…”

Section: M Andmentioning

confidence: 99%

“…However Sparse antenna arrays have certain advantages. Like using sparse antennas, larger aperture can be achieved [8]. There are different methods for designing sparse antenna array.…”

Section: Introductionmentioning

confidence: 99%

A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array

et al. 2020

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In this paper, a multi sample compressive sensing (CS) technique is presented for the direction of arrival (DOA) estimation using sparse antenna array that has applications in several fields including radars and sonars. Two different types of sparse antenna arrays are considered. One is linear sparse array for DOA estimation in one dimension and other is L shaped sparse array for DOA estimation in two dimensions. To make the algorithm robust against impulsive and Gaussian noise, a preprocessing stage is introduced. First, in the preprocessing stage median difference correntropy is used that combines median difference and the generalized correntropy. This suppresses the amplitude of impulsive noise. Second, the strength of weighted moving average filter is exploited before applying the CS technique to make the algorithm more robust. In the CS techniques, the source energy is distributed among the adjacent grid due to grid mismatch. Therefore, a fitness function based on the difference of the source energy among the adjacent grid is introduced. This provides the best discretization value through iterative grid refinement for the grid. The effectiveness and robustness of the proposed method is verified through exhaustive simulations for different number of sources and noise scenarios using one dimensional and two-dimensional sparse array structures.

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“…, . , and the corresponding eigenvectors are; , … … , with the magnitudes depend on the power level of received signals [9].…”

Section: … (13) Eigenvaluesmentioning

confidence: 99%

An Alternative Approach for Angle of Arrival Estimation

Mohammed¹

2015

ETJ

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The Multiple Signal Classification (MUSIC) algorithm is the most popular algorithm to estimate the Angle of Arrival (AOA) of the received signals. This algorithm is based on the calculation of thermal noise Eigen values and their corresponding Eigen vectors and then using them in a matrix form in the MUSIC direction finding (DF) formula. All researches in this field assumed isotropic sensors for array antennas. But for practical application in V/UHF, a practical antenna is to be used like half-wave dipole or any type of wired antenna. When MUSIC algorithm is tested with a well-known half-wave dipole which has a directive pattern in E-plane, a false reading is raised from the two angles coincide with the dipole element axis. It is found that the MUSIC algorithm does not take into consideration the nulls that result from the pattern multiplication between array factor AF (θ) and element pattern f (θ). This paper suggests an alternative approach for angle of arrival estimation. A new DF formula is derived. The suggested approach is based on the use of modified array processor in conjunction with minimum noise variance constrain algorithm as an angle of arrival estimator in spite of interference canceller as it is used for. The results show that the alternative approach gives a quite good result without any false reading or any degradation in the performance of AOA estimation.

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Sparse array DOA estimation in the presence of unknown non-uniform noise

Cited by 5 publications

References 9 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

A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array

An Alternative Approach for Angle of Arrival Estimation

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