Author :
Xu, Guanghan ; Silverstein, Seth D. ; Roy, Richard H. ; Kailath, Thomas
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
fDate :
2/1/1994 12:00:00 AM
Abstract :
Most high-resolution algorithms for sensor array processing require an eigendecomposition, which is a computation that is difficult to implement in parallel and requires O(M3) multiplications for an M×M matrix, corresponding to M sensors. Beamspace transformation is one way of reducing computation and sometimes improving the estimation accuracy. As a consequence of the beamspace transformation performed, however, arrays such as uniform linear arrays commonly used in direction finding lose their displacement invariance structure. As a result, computational complexity may actually increase since the computationally efficient ESPRIT algorithm cannot be applied directly. In this paper, a method for restoring the invariance structure resulting in a beamspace ESPRIT algorithm is described. Asymptotic performance analysis of beamspace ESPRIT and simulation results are presented as well
Keywords :
array signal processing; computational complexity; matrix algebra; asymptotic performance analysis; beamspace ESPRIT algorithm; beamspace transformation; computational complexity; direction finding; direction of arrival estimation; displacement invariance structure; eigendecomposition; estimation accuracy; high-resolution algorithms; multiplications; sensor array processing; uniform linear arrays; Array signal processing; Computational complexity; Direction of arrival estimation; Multiple signal classification; Parameter estimation; Performance analysis; Polynomials; Sensor arrays; Signal processing algorithms; US Government;
Journal_Title :
Signal Processing, IEEE Transactions on
