2018 3rd International Conference on Computer and Communication Systems (ICCCS)

1 / 2018-01-17 17:07:53

2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested

two-dimensional direction finding; nested array; sparse Bayesian learning; matrix decomposition; complexity reduction.

In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested array, one of the major issue is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) signal is obtained by difference coarray corresponding to one dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. By this method, the dimensionality of the sensing matrix and data size can be reduced. Sparse Bayesian learning algorithm is used to estimate one-dimensional angle. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angle is estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. The simulation results show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and the number of samples.
direction