Generalized Coprime Array Configurations for Direction-of-Arrival Estimation

By Si Qin, Yimin D. Zhang, Moeness G. Amin. IEEE Transactions on Signal Processing, 2015

887 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/242b83047fdbf17c14655615153ba0b4e0f2de9d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Arye Nehorai
2. Chun-Lin Liu
3. Mianzhi Wang
4. Yanmei Zhang
5. Shan Cao
6. Yilong Lu
7. Shiwei Ren
8. Jianyan Liu

In Signal Processing

1. Arye Nehorai
2. Chun-Lin Liu
3. Mianzhi Wang
4. Yimin D. Zhang
5. Zhiguo Shi
6. Chengwei Zhou
7. P. Vaidyanathan
8. Yujie Gu

Citations in the corpus

• Super Nested Arrays: Linear Sparse Arrays With Reduced Mutual Coupling—Part I: Fundamentals (2016, background): “All these arrays are capable of resolving O(N 2 ) sources provided with O(N ) sensors [16], [17], [21], [26], [28], and increasing the spatial resolution [16], [18]–[20], [26], [30].”
• Augmented Nested Arrays With Enhanced DOF and Reduced Mutual Coupling (2017, methodology): “The CACIS-Nested array in [23] is slightly different from the nested array in [9].”
• Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation (2018, background): “Although the generalized coprime array configurations [20], [21] and the coprime planar arrays [22] enable further expansion of the number of contiguous virtual sensors, the estimation performance loss is inevitable because the discontiguous virtual sensors are discarded.”
• Coarrays, MUSIC, and the Cramér–Rao Bound (2016, background): “Recently, the development of co-prime arrays [5]–[8] and nested arrays [9]–[11], has generated renewed interest in sparse linear arrays, and it remains to investigate the performance of these arrays.”