Application of Uncorrelated Leaning from Low-Rank Dictionary in Blind Source Separation
Abstract: This paper proposes
a kind of method about signal BOA estimation from the aspect of sparse decomposition.
The whole interested space is divided into several potential angles of arrival
to establish a over-complete directory to convert the estimation problem of
signal DOA to sparse representation problem. A MMV array is formed by data
received from multiple snapshots, then using optimization method of joint sparse
constraint to solve the problem. First, make singular value decomposition on
received data array to connect the each snapshot data, then using the sparse
representation problem of ݈ bounded to solve the problem. To improve the anti-noise
performance of algorithm, the paper applies similar Sigmoid function of two
parameters to approximate ݈ norm. This method applies to the DOA estimation of
narrow-band andbroad-band signal. ܮܵܬെ ܸܵ
ܦshall be used for solving
MMV problem, which achieves joint sparse constraint of all frequency of
reception matrix of broadband signal, to make array elements spacing break through
the limitation of half wavelength and improve resolution of DOA signal.
Author: Liu Sheng
Journal Code: jptkomputergg160057
