Detecting Gaussian Signals Using Coprime Sensor Arrays in Spatially Correlated Gaussian Noise

Bautista, Radienxe; Buck, John R.

doi:10.1109/tsp.2018.2887399

Cited by 13 publications

(7 citation statements)

References 42 publications

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“…, can be sparsely expressed on the entire discrete angle grid as: [22], making full use of all the degrees of freedom of mutual lag and cross lag. Many effective methods in the framework of convex optimization [26][27] and Bayesian sparse learning [28] can be used to solve the sparse reconstruction problem of complex-valued groups [28][29].…”

Section: -D Doa Estimation Methods For Sparse Arraymentioning

confidence: 99%

“…Reference [22] proposed the use of compressed sensing for sparse matrix processing, which reduces the computational complexity of DOA estimation, but it is not used in the MIMO coprime array structure. Reference [23] proposes a fast DOA estimation method with parallel uniform linear arrays, which constructs a sub-array, but when there are many sources, additional matching is required, and the sensors are not fully utilized.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Jin

et al. 2021

Preprint

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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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Section: -D Doa Estimation Methods For Sparse Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Jin

et al. 2021

Preprint

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show abstract

“…. , Q , which can be solved in the sparse reconstruction framework [23], making full use of all the DOF of mutual lag and cross lag. Many effective methods in the framework of convex optimization [27,28] and Bayesian sparse learning [29] can be used to solve the sparse reconstruction problem of complex-valued groups [29,30].…”

Section: -D Doa Estimation Methods For Sparse Arraymentioning

confidence: 99%

“…Bautista and Buck et al [23] proposed the use of compressed sensing for sparse matrix processing, which reduces the computational complexity of DOA estimation, but it is not used in the MIMO coprime array structure. So et al [24] proposed a fast DOA estimation method with parallel uniform linear arrays, which constructs a sub-array, but when there are many sources, additional matching is required, and the sensors are not fully utilized.…”

mentioning

confidence: 99%

2-D DOA estimation method based on coprime array MIMO radar

Zhang

Jin

et al. 2021

EURASIP J. Adv. Signal Process.

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Aiming 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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“…In the case of spatially correlated noise, the noise can be modeled with a covariance matrix given by d qq . The spatial covariance matrix of the noise, which describe the crosscorrelation between noise received by antenna i and j, is [73], [97] :d qq ; m± = 9!c m c ± B " = :…”

Section: Spatially Correlated Noisementioning

confidence: 99%

Cyclostationary beamforming for multiple cycle frequencies estimation and detection

Yang¹

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I would like to express my most sincere gratitude to my supervisor, Prof. Ser Wee, for his professional guidance, advice, encouragement, and continued support for the entirely of my doctoral program. This thesis would not have been possible without his commitment to supervising my doctoral program.I would also like to express my sincere gratitude to Temasek Laboratories @ NTU (TL@NTU) for the sponsorship awarded to me. I am very grateful to TL@NTU for supporting my participation in overseas international conferences. I would like to thank the sensor array team at TL@NTU which includes Dr. SEE Chong Meng, Dr. RAZUL Sirajudeen Gulam and Dr. LEI Lei for their guidance, patience and support of my doctoral program. I would like to thank Ms. Catherine TENG Siew Jiuan who helped me with administrative matters, allowing me to better focus on my doctoral program. I would like to express my deepest gratitude to my parents for their unconditional love to me. I would like to thank my wonderful wife for her physical and emotional support throughout my doctoral program journey. It has been especially difficult with taking care of my two beautiful sons.Finally, I thank GOD for every blessing and strength he has given me.

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Detecting Gaussian Signals Using Coprime Sensor Arrays in Spatially Correlated Gaussian Noise

Cited by 13 publications

References 42 publications

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

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

2-D DOA estimation method based on coprime array MIMO radar

Cyclostationary beamforming for multiple cycle frequencies estimation and detection

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