TY - GEN
T1 - A Tensor Method of Angle Estimation for Bistatic MIMO Radar in the Presence of Spatially Colored Noise and Strongly Correlated Targets
AU - Luo, Shuai
AU - Wang, Yuexian
AU - Han, Chuang
AU - Gong, Yanyun
AU - Li, Jianying
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - This paper is devoted to the angle estimation problem for bistatic multiple-input multiple-output (MIMO) radar under strongly correlated targets and spatially colored noise scenes. Firstly, the cross-covariance matrix is established by using the independent noise vector after matched filtering in time domain, and the components of spatial colored noise are eliminated. Then, the cross-covariance matrix is rearranged into a third-order tensor, and the reconstructed tensor can be obtained by concatenating two third-order tensors. Finally, the Parallel Factor (PARAFAC) decomposition is utilized to resolve angle estimates. Owing to the reconstructed third-order tensor, angle estimation by the proposed method is more accurate than existing methods and has stronger robustness even though there is a strong correlation between targets. Simulation results verify the advantages of our solutions over its cutting-edge counterparts.
AB - This paper is devoted to the angle estimation problem for bistatic multiple-input multiple-output (MIMO) radar under strongly correlated targets and spatially colored noise scenes. Firstly, the cross-covariance matrix is established by using the independent noise vector after matched filtering in time domain, and the components of spatial colored noise are eliminated. Then, the cross-covariance matrix is rearranged into a third-order tensor, and the reconstructed tensor can be obtained by concatenating two third-order tensors. Finally, the Parallel Factor (PARAFAC) decomposition is utilized to resolve angle estimates. Owing to the reconstructed third-order tensor, angle estimation by the proposed method is more accurate than existing methods and has stronger robustness even though there is a strong correlation between targets. Simulation results verify the advantages of our solutions over its cutting-edge counterparts.
KW - bistatic MIMO radar
KW - PARAFAC
KW - spatial colored noise
KW - strongly correlated targets
UR - http://www.scopus.com/inward/record.url?scp=85118466195&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC52875.2021.9564643
DO - 10.1109/ICSPCC52875.2021.9564643
M3 - 会议稿件
AN - SCOPUS:85118466195
T3 - Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
BT - Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
Y2 - 17 August 2021 through 19 August 2021
ER -