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 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

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.

scholarly journals Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5164
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Antenna Array ◽  
Dynamic Range ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Single Step ◽  
Window Function ◽  
Spectral Domain ◽  
Low Sidelobe ◽  
Weighting Window

Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

Strain Sensing through a Passive Wireless Sensor Array

2012 ◽  
Author(s):  
Xiaohua Yi ◽  
Chia-Hung Fang ◽  
James Cooper ◽  
Chunhee Cho ◽  
Rushi Vyas ◽  
...  
Keyword(s):  
Sensor Array ◽  
Wireless Sensor ◽  
Strain Sensing

Enhanced covariances matrix sparse representation method for DOA estimation

2015 ◽  
Vol 51 (16) ◽  
pp. 1288-1290 ◽  
Author(s):  
Wei Cui ◽  
Tong Qian ◽  
Jing Tian
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Representation Method
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