A novel DOA estimation method for closely spaced multiple sources with large power differences

AbstractAiming at the problem that traditional direction of arrival (DOA) estimation methods cannot handle multiple sources with high accuracy while increasing the degrees of freedom (DOF), a new method for 2-D DOA estimation based on coprime array MIMO radar (SA-MIMO-CA) is proposed. First of all, in order to ensure the accuracy of multi-source estimation when the number of elements is finite, a new coprime array model based on MIMO (MIMO-CA) is proposed. This method is based on a new MIMO array-based co-prime array model (MIMO-CA), which improves the accuracy of multi-source estimation when the number of array elements is limited, and obtains a larger array aperture with a smaller number of array elements, and improves the estimation accuracy of 2-D DOA. Finally, the effectiveness and reliability of the proposed SM-MIMO-CA method in improving the DOF of array and DOA accuracy are verified by experiments.

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2-D DOA Estimation Method Based on Coprime Array MIMO Radar

10.21203/rs.3.rs-478458/v1 ◽

2021 ◽

Author(s):

Fei Zhang ◽

Zijing Zhang ◽

Aisuo Jin ◽

Chuantang Ji ◽

Yi Wang

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Mimo Radar ◽

Degree Of Freedom ◽

Estimation Methods ◽

Estimation Accuracy ◽

Two Dimensional ◽

Multiple Sources ◽

Sparse Array ◽

Coprime Array

Abstract Aiming at the problem that traditional Direction of Arrival (DOA) estimation methods cannot handle multiple sources with high accuracy while increasing the degree of freedom, a new method of 2-D DOA estimation based on coprime array MIMO Radar (SA-MIMO-CA). Frist of all, in order to ensure the accuracy of multi-source estimation when the number of elements is finite, a new coprime array model based on MIMO (MIMO-CA) is proposed. The array model uses a special irregular array as the transmitting array and a uniform linear array as the receiving array. Besides, in order to reduce complexity and improve the accuracy of two-dimensional DOA estimation, a new two-dimensional DOA estimation method based on sparse array is proposed. This method uses the sparse array topology of virtual array elements to analyze a larger number of information sources, and combines the compressed sensing method to process the sparse array, and obtains a larger array aperture with a smaller number of array elements, and improves the resolution of the azimuth angle. This method improves the DOA estimation accuracy and reduces the complexity. Finally, experiments verify the effectiveness and reliability of the SA-MIMO-CA method in improving the degree of freedom of the array, reducing the complexity, and improving the accuracy of the DOA.

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DOA estimation method based on sparse representation and constrained optimization

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.02106 ◽

2013 ◽

Vol 32 (8) ◽

pp. 2106-2108

Author(s):

Ying GUO ◽

Cai-yun MENG

Keyword(s):

Constrained Optimization ◽

Sparse Representation ◽

Estimation Method ◽

Doa Estimation

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A Signal Synchronization and Pilot-Aided DOA Estimation Method in the presence of Interferences

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344957 ◽

2020 ◽

Author(s):

Lanxin Wei ◽

Siye Wang ◽

Wenbo Xu ◽

Dong Li ◽

Kun Yang

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Signal Synchronization

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An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽

10.3390/s21134403 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4403

Author(s):

Ji Woong Paik ◽

Joon-Ho Lee ◽

Wooyoung Hong

Keyword(s):

Covariance Matrix ◽

Phased Array ◽

Null Space ◽

Estimation Method ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Improve Performance ◽

Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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The upper bound of multi-source DOA information in sensor array and its application in performance evaluation

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-020-00700-8 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Weilin Tu ◽

Dazhuan Xu ◽

Ying Zhou ◽

Chao Shi

Keyword(s):

Performance Evaluation ◽

Upper Bound ◽

Array Signal Processing ◽

Additive White Gaussian Noise ◽

Doa Estimation ◽

Posteriori Probability ◽

Theoretical Expression ◽

Source Information ◽

Multiple Sources ◽

Dimensional Parameter

Abstract Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform linear array. At high SNRs and under the sparse-source assumption obtained is the upper bound of DOA information contained in K sparse sources which can be regarded as the sum of all single-source information minus the uncertainty of sources’ order logK!. Moreover, because of the uncertainty of multi-sources’ order, the posteriori probability distribution of DOA no longer obeys single peak Gaussian distribution so that the mean square error is unsuitable in evaluating the performance of multi-dimensional parameter estimation. Consequently, entropy error (EE) is used as a new performance evaluation metric, whose relationship with DOA information is given.

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