DOA Estimation for Sources with Large Power Differences

Fang, Qinghe; Jin, Mengzhe; Liu, Weidong; Han, Yujun

doi:10.1155/2021/8862789

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…However, this type of algorithm is extremely complex. Secondly, the jamming jam method (JJM) was proposed by Fang et al and its extended application [9,10]. e core concept is to use the angle information of the signal receiving matrix to establish an interference blocking matrix to eliminate the interference signal part of the received signal covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation Method of Weak Signal under the Compound Background of Strong Interference and Colored Noise

Lin

Zhou

et al. 2022

International Journal of Antennas and Propagation

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The traditional algorithm performing direction of arrival (DOA) estimation under the background of strong interference and colored noise has the problems of low estimation accuracy and small measurement targets. Based on the construction of a fourth-order cumulant (FOC) matrix to suppress colored noise, this paper adopts the extended noise subspace (ENS) algorithm and the fixed projection blocking (FPB) algorithm to estimate the DOA of weak targets. Firstly, a FOC matrix of the received signal vector is established to curb the noise component, and the eigenvalue decomposition is performed. Then, two approaches of weak signal DOA estimation are proposed. One approach is to merge the space where the strong interference steering vector lies into the noise subspace to construct an extended noise subspace, and then, the multisignal classification (MUSIC) algorithm is used to obtain the DOA estimation of the weak signal on the basis of the extended noise subspace. Another approach is to build the orthogonal projection matrix of the interference subspace as the interference blocking matrix, and the receiving array signal is preprocessed, and on the basis of it, the eigen decomposition is performed again to obtain the DOA information of the weak signal. Both algorithms make breakthroughs in the aperture limitation of the traditional algorithm, effectively expand the aperture, and promote the accuracy of estimation. The simulation tests the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

DOA Estimation Method of Weak Signal under the Compound Background of Strong Interference and Colored Noise

Lin

Zhou

et al. 2022

International Journal of Antennas and Propagation

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“…Determining the direction-of-arrival (DOA) of incoming signals is an important research problem in array signal processing, which is widely used in radar, sonar, navigation, wireless communications, and acoustics [1][2][3][4][5][6][7]. Under the uniform white noise assumption, several high resolution DOA estimation approaches are well known to provide high accuracy and excellent asymptotic performance, such as multiple signal classification (MUSIC) [8] and estimation of signal parameters via rotational invariance technique (ESPRIT) [9].…”

Section: Introductionmentioning

confidence: 99%

Sparse Spatial Spectral Fitting with Nonuniform Noise Covariance Matrix Estimation Based on Semidefinite Optimization

Guo

Feng

et al. 2022

Wireless Communications and Mobile Computing

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In general, the azimuth estimation in array signal processing is derived under the assumption of uniform white noise, whose covariance matrix is a scaled identity matrix. However, in practice, the noise can be nonuniform with an arbitrary unknown diagonal covariance matrix. In this paper, the estimation of the noise covariance matrix is formulated into a solution to the semidefinite optimization problem which can obtain a more accurate sensor noise covariance matrix. In the proposed algorithm, the estimated nonuniform noise is subtracted from the sample covariance matrix. The simulation results show that the proposed algorithm can significantly improve the performance of the sparse spectrum fitting algorithm in nonuniform noise case, while the classic SpSF algorithm is used under uniform white noise assumption. The water pool experiments show that there are indeed significant differences in the noise covariance of the sensors.

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