scholarly journals Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 686 ◽  
Author(s):  
Kai Yu ◽  
Ming Yin ◽  
Ji-An Luo ◽  
Yingguan Wang ◽  
Ming Bao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Wireless Sensor ◽  
Array Data ◽  
Joint Sparse Representation

DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

2016 ◽  
Vol 23 (9) ◽  
pp. 1279-1283 ◽  
Author(s):  
Kai Yu ◽  
Yimin D. Zhang ◽  
Ming Bao ◽  
Yu-Hen Hu ◽  
Zhi Wang
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Array Data ◽  
Joint Sparse Representation

scholarly journals DOA Estimation of Strictly Noncircular Sources in Wireless Sensor Array Network via Block Sparse Representation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47500-47508 ◽  
Author(s):  
Liangliang Li ◽  
Tingting Fu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Wireless Sensor

scholarly journals Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4465 ◽  
Author(s):  
Jianfeng Li ◽  
Zheng Li ◽  
Xiaofei Zhang
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Sparse Recovery ◽  
Estimation Methods ◽  
Acoustic Vector Sensor ◽  
Angle Estimation ◽  
Vector Sensor ◽  
Cross Covariance

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

Sign in / Sign up

Export Citation Format

Share Document