Wideband direction-of-arrival (DOA) estimation has been widely used in various active research areas. As a result, many kinds of algorithms have been proposed to address this problem. The covariance matrix of the array output data are utilized by those subspace-based wideband DOA estimation algorithms to acquire super-resolution performance. Since these subspace-based methods use a bank of narrowband filters (NF) to obtain data which are highly correlated in time, they need a sufficient large number of samples to yield accurate estimates at each frequency bin.
Recently, sparse signal representation has been introduced in DOA estimation. Solving multiple measurement vectors (MMV) problem is used to estimate DOA by utilizing the joint sparsity structure of the vectors. And a number of sparse recovery algorithms have been proposed. However, since NF is still involved in the sparse signal representation model, it does not perform well when the number of samples is highly limited. Moreover, wideband DOA estimation cannot be casted as a MMV problem, since every measurement vector in wideband DOA estimation does not share the same basis representative matrix.
To solve this problem, researchers from the Institute of Acoustics, Chinese Academy of Sciences (CAS), the University of Science and Technology of China, and Shanghai Institute of Microsystem and Information Technology, CAS propose a novel algorithm.
They treat the wideband DOA estimation as a block sparsity reconstruction problem to use the joint sparsity property among different blocks. It is proved that better results can be achieved if block sparsity rather than conventional sense is considered in the recovery of block-sparse signals, since additional structures are ignored in conventional methods. Block Orthogonal Matching Pursuit was also proposed to recover block-wise sparse signals, but the resolution of this algorithm is very low.
Whittaker-Shannon interpolation formula instead of NF is used to interpolate highly limited signals more precisely and turn wideband DOA estimation into a block sparsity reconstruction problem. And then, Block FOCUSS (BFOCUSS) is introduced to deal with this problem. The simulation results show the superior performance of BFOCUSS compared with the other algorithms in wideband DOA estimation.
This research is supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA06020201) and the National Natural Science Foundation of China (Grant No. 11174316).
The research has been published in the Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE (3-5 Dec. 2013, pp. 634 – 637).
Contact:
ZHANG Jiawei
Lab of Communication Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China