The matrix pencil method for two-dimensional direction of arrival estimation employing an L-shaped array

1997 ◽  
Vol 45 (11) ◽  
pp. 1693-1694 ◽  
Author(s):  
J.E. Fernandez del Rio ◽  
M.F. Catedra-Perez
Author(s):  
Han Trong Thanh ◽  
Do Trong Tuan ◽  
Nguyen Trong Duc ◽  
Vu Van Yem

In  this  paper,  we  propose  an  approach  to estimate  the  Direction  of  Arrival  (DOA)  of  Radio coherent  incoming  signals  using  the  Total  Forward  – Backward  Matrix  Pencil  algorithm  (TFBMP).  This algorithm  works  directly  on  samples  of  signals impinging  on  an  M  –  element  Uniform  Circular Antenna (UCA) array, which has a smaller size as well as  larger  observation  angle  in  comparison  with  the Uniform  Linear  Antenna  (ULA)  array.  Therefore,  the correlation  between  the  received  signals  does  not significantly  impact  on  its performance  and  efficiency. Furthermore,  this algorithm  can  also  extract  the  DOA information  with  only  one  snapshot  of  signal. Simulation  results  for  DOA  estimation  using  the proposed approach for different situations of  incoming signals  as  well  as  the  number  of  snapshots  in  the presence  of  noise  will  be  assessed  to  verify  its performance.


2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Weiwei Hu ◽  
Aijun Zhang ◽  
Changming Wang

This paper proposes a new method for cross array to estimate two-dimensional direction of arrival (2-D DOA) in the presence of mutual coupling. In this method, the array elements which are affected by the same mutual coupling are chosen onx-axis andz-axis, respectively. Then a new matrix is constructed with the proper entries of cross covariance matrix of the chosen elements outputs onx-axis andz-axis. Propagation method (PM) and rotational invariance techniques for uniform linear array (ULA) are utilized in the constructed matrix to obtain two parameters correlated with elevations and azimuths. While calculating and pairing the two parameters, only once eigendecomposing and several division operations are required with the relationship among the matrix, its eigenvalues, and corresponding eigenvectors. Simulations are presented to validate the performance of the proposed method.


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