TY - GEN
T1 - Decoupled DOA and Polarization Estimation Based on Block 2D Sparse Representation
AU - Shi, Shuli
AU - Xu, Yougen
AU - Zhao, Kang
AU - Huang, Yulin
AU - Liu, Zhiwen
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/6/19
Y1 - 2020/6/19
N2 - A two-dimensional (2D) block sparse representation method is proposed for 2D direction of arrival (DOA) and polarization estimation by using planar sparse vector sensor array. From the second-order statistics of the array output, a virtual uniform rectangular array can be constructed, based on which a 2D sparse representation model is formulated, wherein the two overcomplete bases are each associated with a one-dimensional (1D) angle of the incident signal and matrix sizes of them are a much smaller than that in 1D sparse representation model. Then a block 2D orthogonal matching pursuit algorithm is proposed to recover the block sparse matrix from the observation data of the virtual array. The DOA and polarization parameters can be estimated from the nonzero blocks in the sparse matrix. Computer simulations show the improvements of proposed method than the existing methods in estimation performance and computational complexity.
AB - A two-dimensional (2D) block sparse representation method is proposed for 2D direction of arrival (DOA) and polarization estimation by using planar sparse vector sensor array. From the second-order statistics of the array output, a virtual uniform rectangular array can be constructed, based on which a 2D sparse representation model is formulated, wherein the two overcomplete bases are each associated with a one-dimensional (1D) angle of the incident signal and matrix sizes of them are a much smaller than that in 1D sparse representation model. Then a block 2D orthogonal matching pursuit algorithm is proposed to recover the block sparse matrix from the observation data of the virtual array. The DOA and polarization parameters can be estimated from the nonzero blocks in the sparse matrix. Computer simulations show the improvements of proposed method than the existing methods in estimation performance and computational complexity.
KW - Vector sensor
KW - direction of arrival estimation
KW - polarization estimation
KW - sparse planar array
KW - two-dimensional sparse representation
UR - https://www.scopus.com/pages/publications/85091586490
U2 - 10.1145/3408127.3408143
DO - 10.1145/3408127.3408143
M3 - Conference contribution
AN - SCOPUS:85091586490
T3 - ACM International Conference Proceeding Series
SP - 207
EP - 211
BT - ICDSP 2020 - 2020 4th International Conference on Digital Signal Processing, Proceedings
PB - Association for Computing Machinery
T2 - 4th International Conference on Digital Signal Processing, ICDSP 2020
Y2 - 19 June 2020 through 21 June 2020
ER -