Spatial Compressive Sensing for Direction-of-Arrival Estimation of Multiple Sources using Dynamic Sensor Arrays

2011 ◽  
Vol 47 (3) ◽  
pp. 1754-1769 ◽  
Author(s):  
I. Bilik
Keyword(s):  
Compressive Sensing ◽  
Sensor Arrays ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Multiple Sources

scholarly journals Effect of varying Number of Spacing between Antenna Elements, Snapshots, SNR on AP ML, AP-SSF and ESPRIT Algorithm

2021 ◽  
Vol 11 ◽  
pp. 143-150
Author(s):  
Vinod Kumar ◽  
Sanjeev Kumar Dhull
Keyword(s):  
Signal Processing ◽  
Maximum Likelihood ◽  
Key Words ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Performance Comparison ◽  
Direction Of Arrival Estimation ◽  
Multiple Sources ◽  
Alternating Projection ◽  
Esprit Algorithm

The direction of arrival estimation is the main key problem in array signal processing. In this paper, the alternating projection maximum Likelihood (AP-ML), Alternating projection sub space framework (APSSF) and ESPRIT algorithm are studied. The simulation is performed in MATLAB for single and multiple sources. The effect of the varying number of spacing between antenna elements, number of snapshots and SNR are studied. The performance comparison shows that ESPRIT algorithm performs better as compared to the AP-ML and AP-SSF. Key-Words: - AP-ML, AP-SSF, Direction of Arrival, ESPRIT, Snapshots, SNR

Photonic Direction-of-Arrival Estimation Based on Compressive Sensing

2021 ◽  
Author(s):  
Jingjing Cai ◽  
Xueyan Chang ◽  
Wei Liu ◽  
Tao Shang ◽  
Chao Li
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation

scholarly journals A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hamid Ali Mirza ◽  
Laeeq Aslam ◽  
Muhammad Asif Zahoor Raja ◽  
Naveed Ishtiaq Chaudhary ◽  
Ijaz Mansoor Qureshi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Fitness Function ◽  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Direction Of Arrival Estimation ◽  
Grid Refinement ◽  
Computing Paradigm ◽  
The Difference

In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

scholarly journals Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays

2016 ◽  
Vol 23 (12) ◽  
pp. 1791-1795 ◽  
Author(s):  
Kai Yu ◽  
Ralph E. Hudson ◽  
Yimin D. Zhang ◽  
Kung Yao ◽  
Charles Taylor ◽  
...  
Keyword(s):  
Sensor Arrays ◽  
Direction Of Arrival ◽  
Low Complexity ◽  
Direction Of Arrival Estimation ◽  
Acoustic Sensor
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