TY - GEN
T1 - Direction-of-Arrival and Range Estimation of Near-Field Sources Based on Subspace Fitting
AU - Tong, Yingnan
AU - Zuo, Weiliang
AU - Xin, Jingmin
AU - Zheng, Nanning
AU - Sano, Akira
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - A new algorithm is proposed to address the problem of near-field source localization using weighted subspace fitting (WSF) in this paper. Through separating the two parameters of bearings and ranges, the two-dimensional parameter estimation problem is first transformed into one-dimensional parameter estimation problem. The specific method is to construct a Toeplitz-like correlation matrix by using the anti-diagonal elements of the near-field source signal covariance matrix. Then the subspace fitting algorithm of sparse recovery is used to estimate the direction of arrival (DOA). The estimated direction is substituted back to the original near-field source model. After that, the sparse recovery algorithm based on singular value decomposition can be used to calculate the estimated value of the ranges. Computer simulations verify the excellent performance of the algorithm. In addition, the algorithm has lower requirements for SNR and snapshots.
AB - A new algorithm is proposed to address the problem of near-field source localization using weighted subspace fitting (WSF) in this paper. Through separating the two parameters of bearings and ranges, the two-dimensional parameter estimation problem is first transformed into one-dimensional parameter estimation problem. The specific method is to construct a Toeplitz-like correlation matrix by using the anti-diagonal elements of the near-field source signal covariance matrix. Then the subspace fitting algorithm of sparse recovery is used to estimate the direction of arrival (DOA). The estimated direction is substituted back to the original near-field source model. After that, the sparse recovery algorithm based on singular value decomposition can be used to calculate the estimated value of the ranges. Computer simulations verify the excellent performance of the algorithm. In addition, the algorithm has lower requirements for SNR and snapshots.
KW - Array covariance matrix
KW - Near-field source location
KW - Singular value decomposition
KW - Sparse recovery
KW - Subspace
UR - https://www.scopus.com/pages/publications/85128029646
U2 - 10.1109/CAC53003.2021.9727782
DO - 10.1109/CAC53003.2021.9727782
M3 - 会议稿件
AN - SCOPUS:85128029646
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 5582
EP - 5586
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -