A new 2-D DOA estimation method based on coprime array MIMO radar
Authors: 
Fei Zhang, Zijing Zhang, Aisuo Jin, Luxi YangAuthors Info & Claims
Article No.: 6, Pages 1 - 6
Abstract
Aiming at the problem that traditional Direction of Arrival (DOA) estimation methods cannot handle multi-source with high accuracy while increasing the degree of freedom, proposing a new two-dimensional DOA estimation method based on coprime array MIMO radar (SM-MIMO-CA). Firstly, to ensure the accuracy of multi-source estimation under the finite number of array elements, proposing a new array model based on coprime array MIMO radar (MIMO-CA). Array model uses a special combination of irregular transmitting array and uniform linear array receiving array. Besides, to decrease the complexity and raise the accuracy of two-dimensional DOA estimation, proposing a new DOA estimation method based on coprime array MIMO radar. This method uses the sparse array topology of virtual array elements to analyze a larger number of sources, and combines the compressed sensing method to process the sparse array. This method obtains a larger array aperture with a smaller number of elements, and improves the resolution of the azimuth angle, so the DOA estimation accuracy is improved and the complexity is reduced. Finally, experiments are verified the effectiveness and reliability of the SM-MIMO-CA in improving the degree of freedom of the array, decreasing the complexity, and enhancing the accuracy of DOA.
References
[1]
Atul Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE 802.11 wireless networks. In Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets’04) . IEEE, Los Alamitos, CA, 210–217. https://doi.org/10.1109/BROADNETS.2004.8
[2]
R. Schmidt (1986). Multiple emitter location and signal parameter estimation. in IEEE Transactions on Antennas and Propagation, 34(3), 276-280.
[3]
L. He, (2018). Noncircular Signal DOA Estimation with Reduced Dimension MUSIC for Coprime Linear Array. 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC).
[4]
R. Roy and T. Kailath (1986). ESPRIT-estimation of signal parameters via rotational in variance techniques. in IEEE Trans. On Acoustics, Speech, and Signal Processing,37(7), 984-995.
[5]
P. Ma, J. Li, F. Xu and X. Zhang (2021). Hole-Free Coprime Array for DOA Estimation: Augmented Uniform Co-Array. in IEEE Signal Processing Letters, 28, 36-40.
[6]
X. Li, J. Chen, W. Tan and W. Yang (2019). Gridless DOA estimation method for monostatic MIMO array base on covariance matrix reconstruction. Systems Engineering and Electronics, 42(5), 969-977.
[7]
S. Qin, Y. D. Zhang and M. G. Amin (2020). Improved two-dimensional DOA estimation using parallel coprime arrays. Signal processing, 172(Jul.),107428.1-107428.9.
[8]
C. Liu and P. P. Vaidyanathan (2016). Super nested arrays: Linear sparse arrays with reduced mutual coupling—Part II: High-order extensions. in IEEE Transactions on Signal Processing, 64(16), 4203-4217.
[9]
J. Liu, Y. Zhang, Y. Lu, S. Ren and S. Cao (2017). Augmented nested arrays with enhanced DOF and reduced mutual coupling. IEEE Trans. Signal Process., 65(21), 5549–5563.
[10]
G. Wang, Z. Fei and S. Ren (2021). 2D DOA Estimation Exploiting Vertical Synthetic Planar Arrays. in IEEE Access, 9, 3497-3507.
[11]
S. Qin, Y. D. Zhang and M. G. Amin (2020). Improved two-dimensional DOA estimation using parallel coprime arrays. Signal processing, 172(Jul.),107428.1-107428.9.
[12]
Z. Meng, and W. Zhou, (2020). Robust adaptive beamforming for coprime array with steering vector estimation and covariance matrix reconstruction. IET Communications, 14. 2749-2758.
[13]
F. Wen, Z. Zhang and X. Zhang (2019). CRBs for direction-of-departure and direction-of-arrival estimation in collocated MIMO radar in the presence of unknown spatially coloured noise. IET Radar Sonar Navig.,13 (4), 530–537.
[14]
F. Wen (2019). Computationally efficient DOA estimation algorithm for MIMO radar with imperfect waveforms. IEEE Commun. Lett., 23 (6), 1037–1040.
[15]
J. Li, L. He, Y. He and X. Zhang (2019). Joint direction of arrival estimation and array calibration for coprime MIMO radar. Digital Signal Processing, 94, 67-74,
[16]
W. Zhou, Q. Wang and J. Wang (2019). DOA estimation for monostatic MIMO radar based on unfolded coprime array. Journal of Nanjing University of Posts and Telecommunications: Natural Science Edition, 39(6):1-8.
[17]
R. Bautista and J. R. Buck (2019). Detecting Gaussian Signals Using Coprime Sensor Arrays in Spatially Correlated Gaussian Noise. IEEE Transactions on Signal Processing, 67(5),1296-1306.
[18]
W. So (2003). A fast algorithm for 2-D direction-of-arrival estimation. Signal Processing, 83, 1827-1831.
[19]
N. Tayem and H. M. Kwon (2006). Azimuth and elevation angle estimation with no failure and no eigen decomposition. Signal Processing, 86(1),8-16.
[20]
H. Chen, C. Hou, Q. Wang, (2015). Improved Azimuth/Elevation Angle Estimation Algorithm for Three-Parallel Uniform Linear Arrays. IEEE Antennas & Wireless Propagation Letters, 14,329-332.
[21]
Q. Wu, Y. D. Zhang, M. G. Amin and B. Himed (2014). Complex multitask Bayesian compressive sensing. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 3375-3379.
[22]
G. C. Cawley and N. L. C. Talbot (2005). Sparse Bayesian learning and the relevance multi-layer perceptron network. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., Montreal, QC, Canada, 2,1320-1324.
[23]
J. Li, X. Zhang and C. Han (2012). Improved two-dimensional DOA estimation algorithm for two-parallel uniform linear arrays using propagator method. Signal Processing, 92(12), 3032-3038.
[24]
Z. Yi, X. Xu, Y. A. Sheikh, (2016). A rank-reduction based 2-D DOA estimation algorithm for three parallel uniform linear arrays. Signal Processing, 120(MAR.),305-310.
[25]
J. Li, D. Jiang and X. Zhang (2018). Sparse representation based two-dimensional direction of arrival estimation using co-prime array. Multidimensional Systems and Signal Processing. 29, 35–47.
Index Terms
- A new 2-D DOA estimation method based on coprime array MIMO radar
Index terms have been assigned to the content through auto-classification.
Recommendations
Fragmented coprime arrays with optimal inter subarray spacing for DOA estimation: Increased DOF and reduced mutual coupling
AbstractSparse arrays possess attractive features compared to uniform linear arrays (ULAs) with increased degrees of freedom (DOFs), reduced mutual coupling, and enlarged array aperture, which improves the estimation performance of arrays. Recently, ...
Highlights- A fragmented coprime array concept is introduced to enlarge array aperture.
- Provide the location of holes in the difference coarray of FCAs.
- Two missing interlayer subarrays fills the holes in the difference coarray of FCAs.
- ...
DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar
We propose a new scheme to estimate the directions-of-arrival (DOAs) of mixed coherent and uncorrelated targets exploiting a collocated multiple-input multiple-output (MIMO) radar with transmit/receive coprime arrays. In the proposed scheme, the DOAs of ...
DOA estimation based on the difference and sum coarray for coprime arrays
In this paper, we construct a novel coarray named as the difference and sum (diffsum) coarray by exploiting an improved Conjugate Augmented MUSIC (CAM) estimator, which utilizes both the temporal information and the spatial information. The diffsum ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2021
880 pages
ISBN:9781450390149
DOI:10.1145/3474198
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 December 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
Conference
ICFEICT 2021
ICFEICT 2021: International Conference on Frontiers of Electronics, Information and Computation Technologies
May 21 - 23, 2021
Changsha, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 26Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)4
Reflects downloads up to 20 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format