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2024-02-14
DOA Estimation of Quasi-Stationary Signals Based on a Separated Generalized Nested Array
By
Progress In Electromagnetics Research Letters, Vol. 118, 9-14, 2024
Abstract
This paper proposes a sparse array consisting of two separated generalized nested arrays. The unit element-spacing of each generalized nested array can be adjusted to multiple half-wavelengths of the incident signal. By adjusting the element-spacing, the mutual coupling effect can be greatly reduced. For this array, a direction of arrival (DOA) estimation method of quasi-stationary signals has also been proposed. By using the received signals of the separated generalized nested array, a signal subspace is obtained. Then, this subspace is filled into a higher-order signal subspace to avoid angle ambiguity. Using the higher-order signal subspace, DOAs of all signals can be estimated by spectral peak search. Simulation results show that the proposed separated generalized nested array has better than the conventional nested array performance in DOA estimation.
Citation
Jing Zhao, Sheng Liu, Decheng Wu, and Cheng Zeng, "DOA Estimation of Quasi-Stationary Signals Based on a Separated Generalized Nested Array," Progress In Electromagnetics Research Letters, Vol. 118, 9-14, 2024.
doi:10.2528/PIERL23112901
References

1. Schmidt, R., "Multiple emitter location and signal parameter estimation," IEEE Transactions on Antennas and Propagation, Vol. 34, No. 3, 276-280, Mar. 1986.
doi:10.1109/TAP.1986.1143830

2. Nie, Xi and Liping Li, "A computationally efficient subspace algorithm for 2-D DOA estimation with L-shaped array," IEEE Signal Processing Letters, Vol. 21, No. 8, 971-974, Aug. 2014.
doi:10.1109/LSP.2014.2321791

3. Roy, R. and T. Kailath, "ESPRIT-estimation of signal parameters via rotational invariance techniques," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 7, 984-995, Jul. 1989.
doi:10.1109/29.32276

4. Ma, Wing-Kin, Tsung-Han Hsieh, and Chong-Yung Chi, "DOA estimation of quasi-stationary signals with less sensors than sources and unknown spatial noise covariance: A Khatri–Rao subspace approach," IEEE Transactions on Signal Processing, Vol. 58, No. 4, 2168-2180, Apr. 2010.
doi:10.1109/TSP.2009.2034935

5. Hu, Jie, Wei Li, and Yong Jue Chen, "2-D DOA estimation of quasi-stationary signals via tensor modeling," Applied Mechanics and Materials, Vol. 743, 458-462, 2015.

6. Wu, Fan, Fei Cao, Xurong Zhang, Shikun Lu, and Yanhong Zhang, "DOA estimation of the quasi-stationary signal using sparse reconstruction," IET Radar, Sonar and Navigation, Vol. 16, No. 4, 748-757, Apr. 2022.
doi:10.1049/rsn2.12217

7. Pal, Piya and P. P. Vaidyanathan, "Nested arrays: A novel approach to array processing with enhanced degrees of freedom," IEEE Transactions on Signal Processing, Vol. 58, No. 8, 4167-4181, Aug. 2010.
doi:10.1109/TSP.2010.2049264

8. Pal, Piya and P. P. Vaidyanathan, "Coprime sampling and the MUSIC algorithm," 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 289-294, Sedona, AZ, USA, 2011.

9. Huang, Huiping, Bin Liao, Xiaoye Wang, Xiansheng Guo, and Jianjun Huang, "A new nested array configuration with increased degrees of freedom," IEEE Access, Vol. 6, 1490-1497, 2017.
doi:10.1109/ACCESS.2017.2779171

10. Liu, Sheng, Jing Zhao, Decheng Wu, and Hailin Cao, "Grade nested array with increased degrees of freedom for quasi-stationary signals," Progress In Electromagnetics Research Letters, Vol. 80, 75-82, 2018.

11. Liu, Sheng, Hailin Cao, Decheng Wu, and Xiyuan Chen, "Generalized array architecture with multiple sub-arrays and hole-repair algorithm for DOA estimation," Computers, Materials & Continua, Vol. 64, No. 1, 589-605, 2020.
doi:10.32604/cmc.2020.09964

12. Liu, Sheng, Jing Zhao, Decheng Wu, Yiwang Huang, and Linli Xia, "An unambiguous 2D DOA estimation algorithm by a large-space L-shaped array," Circuits, Systems, and Signal Processing, Vol. 42, No. 11, 6614-6635, 2023.