Abstract
A unitary transformation-based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals. The problem is solved by reorganizing the covariance matrix into a block Hankel one for decorrelation first and then reconstructing a new matrix to facilitate the unitary transformation. By multiplying unitary matrices, eigenvalue decomposition and singular value decomposition are both transformed into real-valued, so that the computational complexity can be reduced significantly. In addition, a fast and computationally attractive realization of the 2-D unitary transformation is given by making a Kronecker product of the 1-D matrices. Compared with the existing 2-D algorithms, our scheme is more efficient in computation and less restrictive on the array geometry. The processing of the received data matrix before unitary transformation combines the estimation of signal parameters via rotational invariance techniques (ESPRIT)-Like method and the forward-backward averaging, which can decorrelate the impinging signals more thoroughly. Simulation results and computational order analysis are presented to verify the validity and effectiveness of the proposed algorithm.
Highlights
Two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals has received much attention in many applications, such as radar, wireless communication and sonar in the multipath environment [1,2,3,4,5]
As the real-valued processing with a uniform linear array (ULA) provides the important preliminary knowledge to our new algorithm, we give a quick review of the definition of the unitary matrix and the real-valued processing based on it, which have been widely used in certain kinds of unitary transformation algorithms ([12,13] etc.)
To retain the 2-D DOA estimation real-valued and reduce the computational complexity, we develop a simple implement of the 2-D unitary ESPRIT based on the 1-D solution [12]
Summary
Two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals has received much attention in many applications, such as radar, wireless communication and sonar in the multipath environment [1,2,3,4,5]. In order to reduce the computational complexity, an efficient method is performed by Hua [8] This method, called the matrix enhancement and matrix pencil (MEMP) algorithm, exploits the structure inherent in an enhanced matrix from the original data. Based on the block Hankel matrix obtained from [11], we preprocess it through a forward-backward average-like method convenient for unitary transformation. It can transform the complex computations into real-valued ones and provide significant computational savings. Simulation results will show that the real computations required for our new algorithm are much less than that of the 2-D ESPRIT-like method It becomes especially obvious when the dimensionality of the Hankel matrix tends to be large. We show that the variance of the estimates from our proposed method is close to the Cramer-Rao bound, and the resolution ability is superior to the others for the forward-backward average processing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
