Vol. 113
Latest Volume
All Volumes
PIERM 130 [2024] PIERM 129 [2024] PIERM 128 [2024] PIERM 127 [2024] PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2022-09-23
A Sparse Array Design Method Based on Direct-Connection of 4 Uniform Linear Arrays
By
Progress In Electromagnetics Research M, Vol. 113, 213-224, 2022
Abstract
In order to obtain the analytical expression of the position of sparse array sensors under the condition of a given total array sensor number, a sparse array design method based on direct-connection of 4 uniform linear arrays (DCUA4) is proposed. By using the only known parameter of the total array sensor number, the sensor number and spacing parameters of four subarrays are obtained by mathematical operation, then the four subarrays are directly connected to realize the design of sparse array. It is proved that the aperture of the sparse array is large, and there are no holes. Because all the sensors are allocated to four subarrays, the number of small spacing sensor pairs in the array is controlled The performance of the proposed array is simulated based on the spatial smoothing MUSIC (SS-MUSIC) algorithm. The simulation results show that the proposed DCUA4 can produce a large virtual array aperture, realize high-precision direction of arrival (DOA) estimation under underdetermined conditions, and resist the influence of low mutual coupling.
Citation
Liye Zhang, Weijia Cui, Chunxiao Jian, Bin Ba, and Hao Li, "A Sparse Array Design Method Based on Direct-Connection of 4 Uniform Linear Arrays," Progress In Electromagnetics Research M, Vol. 113, 213-224, 2022.
doi:10.2528/PIERM22070402
References

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

2. 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, 1989.
doi:10.1109/29.32276

3. Gupta, I. and A. Ksienski, "Effect of mutual coupling on the performance of adaptive arrays," IEEE Transactions on Antennas and Propagation, Vol. 31, No. 5, 785-791, 1983.
doi:10.1109/TAP.1983.1143128

4. Ma, W., T. Hsieh, and C. 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, 2010.
doi:10.1109/TSP.2009.2034935

5. Vaidyanathan, P. P. and P. Pal, "Sparse sensing with co-prime samplers and arrays," IEEE Transactions on Signal Processing, Vol. 59, No. 2, 573-586, 2011.
doi:10.1109/TSP.2010.2089682

6. Moffet, A., "Minimum-redundancy linear arrays," IEEE Transactions on Antennas and Propagation, Vol. 16, No. 2, 172-175, 1968.
doi:10.1109/TAP.1968.1139138

7. Vaidyanathan, P. P. and P. Pal, "Sparse sensing with co-prime samplers and arrays," IEEE Transactions on Signal Processing, Vol. 59, No. 2, 573-586, 2011.
doi:10.1109/TSP.2010.2089682

8. Pal, P. 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, 2010.
doi:10.1109/TSP.2010.2049264

9. Ren, S., W. Wang, and Z. Chen, "DOA estimation exploiting unified coprime array with multi-period subarrays," 2016 CIE International Conference on Radar (RADAR), 2016.

10. Raza, A., W. Liu, and Q. Shen, "Thinned coprime array for second-order difference co-array generation with reduced mutual coupling," IEEE Transactions on Signal Processing, Vol. 67, No. 8, 2052-2065, 2019.
doi:10.1109/TSP.2019.2901380

11. Zheng, W., X. Zhang, J. Li, and J. Shi, "Extensions of co-prime array for improved DOA estimation with hole filling strategy," IEEE Sensors Journal, Vol. 21, No. 5, 6724-6732, 2021.
doi:10.1109/JSEN.2020.3036490

12. Ma, P., J. Li, G. Zhao, and X. Zhang, "CAP-3 coprime array for DOA estimation with enhanced uniform degrees of freedom and reduced mutual coupling," IEEE Communications Letters, Vol. 67, No. 8, 1872-1875, 2021.
doi:10.1109/LCOMM.2021.3057403

13. Liu, J., Y. Zhang, Y. Lu, S. Ren, and S. Cao, "Augmented nested arrays with enhanced DOF and reduced mutual coupling," IEEE Transactions on Signal Processing, Vol. 65, No. 21, 5549-5563, 2017.
doi:10.1109/TSP.2017.2736493

14. Shan, T., M. Wax, and T. Kailath, "On spatial smoothing for direction-of-arrival estimation of coherent signals," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 33, No. 4, 806-811, 1985.
doi:10.1109/TASSP.1985.1164649

15. Svantesson, T., "Mutual coupling compensation using subspace fitting," Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No.00EX410), 494-498, 2000.
doi:10.1109/SAM.2000.878058

16. Liu, C. and P. P. Vaidyanathan, "Super nested arrays: Linear sparse arrays with reduced mutual coupling --- Part I: Fundamentals," IEEE Transactions on Signal Processing, Vol. 64, No. 15, 3997-4012, 2016.
doi:10.1109/TSP.2016.2558159