Sparse Array Design Methods Based on Hole Analysis of the Coprime Array
-
摘要: 针对互质阵列产生连续延迟较少且冗余度高的问题，该文提出了两种基于互质阵列的稀疏设计方法。首先，通过分析阵元位置对互质阵列差分共阵总延迟和连续延迟影响，得出互质阵列在去掉特定阵元后，将不改变连续延迟拓扑。然后，优化传感器阵列布局，在保持整个阵列的阵元数不变的条件下，增加阵列连续延迟数量。其后，分别推得了两种提出阵列设计方法的连续延迟和自由度相关的数学表达式。最后，以相同物理传感器和相同估计方法开展对比仿真，验证提出稀疏阵列设计的DOA估计性能。Abstract: For the problem of a low number of consecutive lags and high redundancy of sensors in the coprime array, two sparse arrays are proposed in this paper. First, by analyzing the influence of the sensor positions on the unique lags and consecutive lags of the difference coarray, it is concluded that the range of the consecutive lags in the coprime array is not changed after removing the specific sensors. Then, entire array structure is optimized while keeping the number of sensors unchanged, increasing the number of consecutive lags. Afterward, mathematical expressions of the consecutive lags and degree of freedoms of the proposed arrays are derived respectively. Finally, simulations are carried out with the same physical sensors and the identical estimation method to verify the DOA estimation performance of the proposed sparse arrays.
-
Key words:
- Coprime array /
- Sparse array /
- Correlation lags /
- Direction-Of-Arrival (DOA) estimation
-
[1] 孙兵, 阮怀林, 吴晨曦, 等. 基于Toeplitz协方差矩阵重构的互质阵列DOA估计方法[J]. 电子与信息学报, 2019, 41(8): 1924–1930. doi: 10.11999/JEIT181041SUN Bing, RUAN Huailin, WU Chenxi, et al. Direction of arrival estimation with coprime array based on Toeplitz covariance matrix reconstruction[J]. Journal of Electronics &Information Technology, 2019, 41(8): 1924–1930. doi: 10.11999/JEIT181041 [2] WANG Xinghua, CHEN Zhenhong, REN Shiwei, et al. DOA estimation based on the difference and sum coarray for coprime arrays[J]. Digital Signal Processing, 2017, 69: 22–31. doi: 10.1016/j.dsp.2017.06.013 [3] SCHMIDT R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276–280. doi: 10.1109/TAP.1986.1143830 [4] LIU Chunlin and VAIDYANATHAN P P. Remarks on the spatial smoothing step in coarray MUSIC[J]. IEEE Signal Processing Letters, 2015, 22(9): 1438–1442. doi: 10.1109/LSP.2015.2409153 [5] PAN Jingjing, SUN Meng, WANG Yide, et al. An enhanced spatial smoothing technique with ESPRIT algorithm for direction of arrival estimation in coherent scenarios[J]. IEEE Transactions on Signal Processing, 2020, 68: 3635–3643. doi: 10.1109/TSP.2020.2994514 [6] CHEN Hua, HOU Chunping, ZHU Weiping, et al. ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization[J] Signal Processing, 2017, 141: 48–56. doi: 10.1016/j.sigpro.2017.05.024. [7] BOUDAHER E, AHMAD F, AMIN M G, et al. Mutual coupling effect and compensation in non-uniform arrays for direction-of-arrival estimation[J]. Digital Signal Processing, 2017, 61: 3–14. doi: 10.1016/j.dsp.2016.06.005 [8] TANG Mang, SHU Ting, HE Jin, et al. Direction-finding with spread array of identical doublets[J]. IEEE Communications Letters, 2021, 25(1): 142–146. doi: 10.1109/LCOMM.2020.3026367 [9] MOFFET A. Minimum-redundancy linear arrays[J]. IEEE Transactions on Antennas and Propagation, 1968, 16(2): 172–175. doi: 10.1109/TAP.1968.1139138 [10] PAL P and VAIDYANATHAN P P. Nested arrays: A novel approach to array processing with enhanced degrees of freedom[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 4167–4181. doi: 10.1109/TSP.2010.2049264 [11] ZHENG Zhi, YANG Chaolin, WANG Wenqin, et al. Robust DOA estimation against mutual coupling with nested array[J]. IEEE Signal Processing Letters, 2020, 27: 1360–1364. doi: 10.1109/LSP.2020.3011314 [12] VAIDYANATHAN P P and PAL P. Sparse sensing with co-prime samplers and arrays[J]. IEEE Transactions on Signal Processing, 2011, 59(2): 573–586. doi: 10.1109/TSP.2010.2089682 [13] ALAWSH S A and MUQAIBEL A H. Multi-level prime array for sparse sampling[J]. IET Signal Processing, 2018, 12(6): 688–699. doi: 10.1049/iet-spr.2017.0252 [14] QIN Sin, ZHANG Y D, and AMIN M G. Generalized coprime array configurations for direction-of-arrival estimation[J]. IEEE Transactions on Signal Processing, 2015, 63(6): 1377–1390. doi: 10.1109/TSP.2015.2393838 [15] ZHOU Chengwei, GU Yujie, ZHANG Y D, et al. Compressive sensing-based coprime array direction-of-arrival estimation[J]. IET Communications, 2017, 11(11): 1719–1724. doi: 10.1049/iet-com.2016.1048 [16] GUO Muran, ZHANG Y D, and CHEN Tao. DOA estimation using compressed sparse array[J]. IEEE Transactions on Signal Processing, 2018, 66(15): 4133–4146. doi: 10.1109/TSP.2018.2847645 [17] LIU Chunlin, VAIDYANATHAN P P, and PAL P. Coprime coarray interpolation for DOA estimation via nuclear norm minimization[C]. 2016 IEEE International Symposium on Circuits and Systems, Montreal, Canada, 2016: 2639–2642. doi: 10.1109/ISCAS.2016.7539135. [18] LIU Ke and ZHANG Y D. Coprime array-based DOA estimation in unknown nonuniform noise environment[J]. Digital Signal Processing, 2018, 79: 66–74. doi: 10.1016/j.dsp.2018.04.003 [19] WANG Mianzhi and NEHORAI A. Coarrays, MUSIC, and the Cramér–rao bound[J]. IEEE Transactions on Signal Processing, 2017, 65(4): 933–946. doi: 10.1109/TSP.2016.2626255 -
下载:
图(7)
计量
- 文章访问数: 782
- HTML全文浏览量: 631
- PDF下载量: 121
- 被引次数: 0
